Datasets:
Add the teaser to the card; replace code/README.md walkthrough with a short overview
d569bc9 verified | pretty_name: OccludeBench | |
| language: | |
| - en | |
| task_categories: | |
| - visual-question-answering | |
| - image-to-text | |
| size_categories: | |
| - n<1K | |
| tags: | |
| - occlusion | |
| - visual-reasoning | |
| - shadows | |
| - reflections | |
| - vision-language-models | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: test | |
| path: images/sec3_occludebench/occluded/* | |
| # OccludeBench: Can VLMs Find What's Missing? | |
| <p align="center"> | |
| <img src="https://huggingface.co/datasets/afs07fda89sdfas90/data/resolve/main/assets/teaser.png" alt="OccludeBench teaser. Top: a direct example (answer: balance ball) and an inferential example (answer: chimney), next to each model's accuracy on direct versus inferential images; every model's gap is larger than the humans' 5.1 points. Bottom: the four OccludeTrace stages (detect cue, recognize cue, map to source, recover in context); only the last stage separates solved from failed images, 91% vs. 50%." width="100%"> | |
| </p> | |
| OccludeBench tests whether vision-language models can recover an object hidden from view. Each of the 378 photographs has one object fully covered by a mask, and the model is asked: | |
| > What is the primary object that is completely occluded in this image? Be as specific as you can. | |
| In **direct** images (197) the object can only be recovered from its shadow or reflection; in **inferential** images (181) it can be inferred from the rest of the scene. Direct images are specular reflections (94), diffuse reflections (56) or shadows (47); inferential images cover technology (45), household (44), culture (37), activities (31) and nature (24). | |
| Answers are open-ended. A response counts as correct if either of two LLM judges (GPT-5.4, Claude Opus 4.6) accepts it, which agrees with author labels on 98% of items. | |
| ## Loading | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("afs07fda89sdfas90/data", split="test") # occluded images + id, type, category, answer | |
| ``` | |
| Or read `annotations.json` directly and open the image paths it lists. | |
| ## Structure | |
| ``` | |
| annotations.json # One record per image | |
| images/ | |
| sec3_occludebench/occluded/ # 378 Benchmark input: object behind a black, yellow-bordered mask | |
| sec3_occludebench/original/ # 378 Unedited photographs | |
| sec4_2_why_vlms_fail/seeing_failure/ # 136 Direct images with the cue region outlined | |
| sec4_2_why_vlms_fail/stage2_zoomed_recognition/ # 153 Cue regions cropped from direct images | |
| sec4_2_why_vlms_fail/stage3_source_mapping/ # 107 Four candidate regions masked and labeled A–D | |
| appendix/box_sensitivity/ # 378 Blue-filled mask, for the mask-style check | |
| code/ # Evaluation, grading and analysis code | |
| ``` | |
| Images are named by item `id` (e.g. `pic0001.png`); crops add a suffix (`pic0002-1.png`). | |
| ## Annotations | |
| | Field | Description | | |
| |---|---| | |
| | `id` | Image id, `pic0001`–`pic0378` | | |
| | `type` | `direct` or `inferential` | | |
| | `category` | Direct: `specular reflection`, `diffuse reflection`, `shadow`. Inferential: `technology`, `household`, `culture`, `activities`, `nature` | | |
| | `fine_category` | Finer annotation label the category was merged from | | |
| | `answer` | Reference answer | | |
| | `occluders` | Mask geometry in original-image pixels: `bbox` (`x1`, `y1`, `x2`, `y2`) or `polygon` (`points`) | | |
| | `images` | Paths to this item's image in each folder (`null` where a variant does not exist) | | |
| | `cue_crops` | Crops of the shadow/reflection region: `image` path and crop `box` (`x`, `y`, `w`, `h`) in original-image pixels. Empty for items without a crop | | |
| | `reverse_mapping` | Source-mapping `image` and the `answer` letter of the region hiding the object, or `null` | | |
| | `source_url` | Where the photograph was found | | |
| | `annotator_note` | Annotator's rationale for the answer | | |
| ## Reproducing the experiments | |
| ```bash | |
| cd code | |
| pip install -r requirements.txt | |
| cp .env.example .env # API keys | |
| python prepare_data.py # lays out the images the way the scripts expect | |
| ``` | |
| See `code/README.md` for the main commands and the code layout. | |
| ## Notes | |
| - `pic0360`: its occluded image was made from a differently cropped copy of the photograph, so its `occluders` coordinates match its original and box-sensitivity images but not its occluded image. | |
| - Images come from the web, Visual Genome, and the authors' own photographs. Web images remain under their original owners' terms; see `source_url`. | |
| ## Citation | |
| ```bibtex | |
| @misc{cekinmez2026occludebench, | |
| title={OccludeBench: Can VLMs Find What's Missing?}, | |
| author={Cekinmez, Jasin and Wu, Addison J. and Mitsuhashi, Ryo and Cai, Linrong and Fu, Xingyu and Liu, Zhuang}, | |
| year={2026} | |
| } | |
| ``` | |