Datasets:

afs07fda89sdfas90
/

data

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?

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

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

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

@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}
}