| --- |
| language: |
| - en |
| license: cc-by-4.0 |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - video-text-to-text |
| tags: |
| - video-understanding |
| - temporal-reasoning |
| - counting |
| - benchmark |
| --- |
| |
| # VCBench: A Streaming Counting Benchmark for Spatial-Temporal State Maintenance in Long Videos |
|
|
| [**Paper**](https://huggingface.co/papers/2603.12703) | [**Code**](https://github.com/buaaplay/VCBench) | [**Dataset**](https://huggingface.co/datasets/buaaplay/VCBench) |
|
|
| VCBench is a streaming counting benchmark that repositions counting as a minimal probe for diagnosing **spatial-temporal state maintenance** capability in video-language models. By querying models at multiple timepoints during video playback, VCBench observes how model predictions evolve rather than checking isolated answers. |
|
|
| ## Task Taxonomy |
|
|
| VCBench decomposes state maintenance into 8 fine-grained subcategories across two dimensions: |
|
|
| ### Object Counting (tracking entities) |
| | Subcategory | Description | |
| |-------------|-------------| |
| | **O1-Snap** | How many objects are visible *at this moment*? | |
| | **O1-Delta** | How many objects appeared in the *past N seconds*? | |
| | **O2-Unique** | How many *different* individuals have appeared so far? | |
| | **O2-Gain** | How many *new* individuals appeared in the past N seconds? | |
|
|
| ### Event Counting (tracking actions) |
| | Subcategory | Description | |
| |-------------|-------------| |
| | **E1-Action** | How many times has an atomic action occurred so far? | |
| | **E1-Transit** | How many scene transitions have occurred so far? | |
| | **E2-Episode** | How many activity segments have occurred so far? | |
| | **E2-Periodic** | How many complete cycles of a periodic action so far? | |
|
|
| ## Dataset Summary |
|
|
| - **Total Videos**: 406 source videos (generating 4,574 clipped segments) |
| - **Total Size**: ~80 GB |
| - **Annotations**: 1,000 counting questions with 4,576 streaming query points and 10,071 frame-by-frame annotations. |
| - **Sources**: YouTube, ARKitScenes, ScanNet, ScanNet++, Ego4D, RoomTour3D, CODa, OmniWorld, and physics simulations. |
|
|
| ## Usage |
|
|
| ### Download via CLI |
|
|
| You can download the dataset using the `huggingface-cli`: |
|
|
| ```bash |
| huggingface-cli download buaaplay/VCBench --repo-type dataset --local-dir data/videos |
| ``` |
|
|
| The `chunkedVideos/` directory contains 4,576 video clips (one per query point), each truncated to the query timestamp. |
|
|
| ### Evaluation |
|
|
| To compute metrics (GPA, MoC, UDA) on results using the official evaluation scripts: |
|
|
| ```bash |
| # Compute metrics on provided results |
| python eval/compute_metrics.py results/vcbench_gemini3flash_unified.jsonl data/vcbench_eval.jsonl |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{vcbench2025, |
| title={VCBench: A Streaming Counting Benchmark for Spatial-Temporal State Maintenance in Long Videos}, |
| author={Liu, Pengyiang and Shi, Zhongyue and Hao, Hongye and Fu, Qi and Bi, Xueting and Zhang, Siwei and Hu, Xiaoyang and Wang, Zitian and Huang, Linjiang and Liu, Si}, |
| year={2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset and code are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). |
