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Datasets:
countvideobench
/
CountVideoBench

Tasks:
Video-Text-to-Text
Visual Question Answering
Modalities:
Video
Languages:
English
Size:
1K<n<10K
ArXiv:
Tags:
video
counting
benchmark
video-language-models
Synthetic
License:
Dataset card Files
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  1. README.md +127 -0
  2. single_class/05_entities/vid_000001.mp4 +3 -0
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README.md CHANGED
@@ -1,3 +1,130 @@
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  ---
 
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  license: cc-by-4.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ pretty_name: CountVideoBench
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  license: cc-by-4.0
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+ language:
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+ - en
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+ task_categories:
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+ - video-text-to-text
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+ - visual-question-answering
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+ size_categories:
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+ - 1K<n<10K
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+ tags:
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+ - video
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+ - counting
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+ - benchmark
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+ - video-language-models
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+ - synthetic
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+ - blender
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+ configs:
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+ - config_name: single_class
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+ default: true
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+ drop_labels: true
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+ data_files:
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+ - split: test
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+ path: "single_class/**"
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  ---
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+
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+ # CountVideoBench
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+
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+ **A benchmark to evaluate prediction biases on the object counting task by video-language models.**
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+
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+ Marc Chapus, Carlos Crispim-Junior, Véronique Eglin, Atilla Baskurt
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+ LIRIS, UMR 5205 — INSA Lyon, Université Lumière Lyon 2, CNRS, Université Claude Bernard Lyon 1
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+
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+ **BMVC 2026**
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+
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+ ## Dataset summary
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+
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+ CountVideoBench contains **2,600 synthetic videos** rendered with Blender, each showing a known number of identical objects. It is designed to isolate *cardinality* as the variable under study:
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+
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+ - **Balanced by cardinality**: 26 counts between 5 and 50, with exactly 100 videos per count. The counts are organised in clusters around six anchors (5, 10, 20, 30, 40, 50), each anchor with its neighbours at ±1 and ±2, so that accuracy can be compared across near-identical inputs:
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+ `5–12, 18–22, 28–32, 38–42, 48–50`.
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+ - **Recognition kept simple**: each video contains a single object class among 10 (2 shapes × 5 colours), with exactly 10 videos per class and per count.
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+ - **Static objects, moving camera**: objects are static and the camera rotates in place. The white background carries a regular line pattern, and object surfaces a low-amplitude procedural noise, so that densely packed objects stay visually distinct.
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+ - **Exact ground truth**: counts come from the generation process, with no human annotation.
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+
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+ The benchmark is far from saturated: humans reach 90.8% exact-match accuracy, while no model evaluated in the paper exceeds 27.6%.
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+
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+ ## Example
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+
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+ ![Preview of vid_001001: 20 red cubes, rotating camera](https://countvideobench.github.io/static/vid_001001.webp)
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+
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+ **Video**: [`single_class/20_entities/vid_001001.mp4`](single_class/20_entities/vid_001001.mp4) (preview above, downscaled) — red cubes, 150 frames.
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+
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+ **Question**: How many red cubes are in this video?
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+
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+ **Answer**: 20
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+
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+ The `question` field of each video holds the exact prompt used in the paper. Using it unchanged keeps results comparable with those reported there.
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+
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+ ## Dataset structure
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+
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+ ```
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+ single_class/
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+ ├── metadata.csv
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+ ├── 05_entities/
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+ │ ├── vid_000001.mp4
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+ │ ├── ...
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+ │ └── vid_000100.mp4
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+ ├── 06_entities/
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+ ├── ...
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+ └── 50_entities/
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+ ```
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+
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+ Each folder holds the 100 videos of one count. `metadata.csv` has one row per video:
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+
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+ | Field | Type | Description |
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+ | ----------- | ------ | ------------------------------------------------ |
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+ | `file_name` | string | Path to the video, relative to `single_class/` |
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+ | `video_id` | string | Unique identifier (`vid_000001` to `vid_002600`) |
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+ | `count` | int | Ground-truth number of objects |
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+ | `shape` | string | `cube` or `sphere` |
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+ | `color` | string | `red`, `green`, `blue`, `yellow` or `purple` |
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+ | `question` | string | Question asked to the model |
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+
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+ Videos: MP4, 1920 × 1080, 30 fps, 5 seconds (150 frames). The full clips are provided so that users can choose their own frame sampling strategy.
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+
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+ ## Usage
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ ds = load_dataset("countvideobench/CountVideoBench", "single_class", split="test")
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+ print(ds[0]["count"], ds[0]["question"])
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+ ```
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+
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+ To download the raw files instead:
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+
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+ ```bash
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+ hf download countvideobench/CountVideoBench --repo-type dataset --local-dir CountVideoBench
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+ ```
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+
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+ ## Intended uses
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+
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+ - Evaluating object counting in video-language models.
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+ - Diagnosing prediction biases per cardinality (preferred values, peaks and collapses in accuracy).
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+ - Comparing models under identical, controlled conditions.
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+
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+ CountVideoBench is an evaluation benchmark: please do not use it as training data, so that reported results remain comparable.
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+
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+ ## Generation
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+
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+ Videos were rendered procedurally with Blender. All generation parameters (rendering, camera, objects, placement, physics, lighting) are documented in Appendix B of the paper.
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+
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+ ## Citation
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+
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+ If you use CountVideoBench, please cite:
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+
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+ ```bibtex
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+ @inproceedings{chapus2026countvideobench,
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+ author = {Chapus, Marc and Crispim-Junior, Carlos and Eglin, V{\'e}ronique and Baskurt, Atilla},
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+ title = {{CountVideoBench}: A Benchmark to Evaluate Prediction Biases on the Object Counting task by Video-Language Models},
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+ booktitle = {37th British Machine Vision Conference 2026, {BMVC} 2026, Lancaster, UK, November 23-26, 2026},
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+ publisher = {BMVA},
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+ year = {2026}
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+ }
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+ ```
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+
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+ ## Contact
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+
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+ Marc Chapus — marc.chapus@liris.cnrs.fr
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