Add paper link, GitHub link, and update metadata for VCBench

Hi! I'm Niels, part of the community science team at Hugging Face. I've opened this PR to improve the dataset card for VCBench:
- Linked the associated paper: [VCBench: A Streaming Counting Benchmark for Spatial-Temporal State Maintenance in Long Videos](https://huggingface.co/papers/2603.12703).
- Added the official GitHub repository link.
- Updated the `task_categories` to `video-text-to-text`.
- Corrected the license to `cc-by-4.0` as specified in the official GitHub README.
- Included the download instructions from the repository.

README.md

@@ -1,157 +1,82 @@
 ---
-license: mit
-task_categories:
-- video-classification
-- question-answering
 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
-size_categories:
-- 1K<n<10K
 ---
 
-# VCBench: Clipped Videos Dataset
-
-## Dataset Description
-
-This dataset contains **4,574 clipped video segments** from the VCBench (Video Counting Benchmark), designed for evaluating spatial-temporal state maintenance capabilities in video understanding models.
-
-### Dataset Summary
-
-- **Total Videos**: 4,574 clips
-- **Total Size**: ~80 GB
-- **Video Format**: MP4 (H.264)
-- **Categories**: 8 subcategories across object counting and event counting tasks
-
-### Categories
-
-**Object Counting (2,297 clips)**:
-- `O1-Snap`: Current-state snapshot (252 clips)
-- `O1-Delta`: Current-state delta (98 clips)
-- `O2-Unique`: Global unique counting (1,869 clips)
-- `O2-Gain`: Windowed gain counting (78 clips)
-
-**Event Counting (2,277 clips)**:
-- `E1-Action`: Instantaneous action (1,281 clips)
-- `E1-Transit`: State transition (205 clips)
-- `E2-Periodic`: Periodic action (280 clips)
-- `E2-Episode`: Episodic segment (511 clips)
-
-## File Naming Convention
+# VCBench: A Streaming Counting Benchmark for Spatial-Temporal State Maintenance in Long Videos
 
-### Multi-query clips
-Format: `{category}_{question_id}_{query_index}.mp4`
+[**Paper**](https://huggingface.co/papers/2603.12703) | [**Code**](https://github.com/buaaplay/VCBench) | [**Dataset**](https://huggingface.co/datasets/buaaplay/VCBench)
 
-Example: `e1action_0000_00.mp4`, `e1action_0000_01.mp4`
+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.
 
-### Single-query clips
-Format: `{category}_{question_id}.mp4`
+## Task Taxonomy
 
-Example: `o1delta_0007.mp4`, `o2gain_0000.mp4`
+VCBench decomposes state maintenance into 8 fine-grained subcategories across two dimensions:
 
-## Video Properties
+### 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? |
 
-- **Encoding**: H.264 (using `-c copy` for lossless clipping)
-- **Frame Rates**: Preserved from source (3fps, 24fps, 25fps, 30fps, 60fps)
-- **Duration Accuracy**: ±0.1s from annotation timestamps
-- **Quality**: Original quality maintained (no re-encoding)
+### 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? |
 
-## Source Datasets
+## Dataset Summary
 
-Videos are clipped from multiple source datasets:
-- YouTube walking tours and sports videos
-- RoomTour3D (indoor navigation)
-- Ego4D (first-person view)
-- ScanNet, ScanNetPP, ARKitScenes (3D indoor scenes)
-- TOMATO, CODa, OmniWorld (temporal reasoning)
-- Simulated physics videos
+- **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
 
-### Loading with Python
-
-```python
-from huggingface_hub import hf_hub_download
-import cv2
+### Download via CLI
 
-# Download a specific video
-video_path = hf_hub_download(
-    repo_id="YOUR_USERNAME/VCBench",
-    filename="e1action_0000_00.mp4",
-    repo_type="dataset"
-)
-
-# Load with OpenCV
-cap = cv2.VideoCapture(video_path)
-```
-
-### Batch Download
+You can download the dataset using the `huggingface-cli`:
 
 ```bash
-# Install huggingface-cli
-pip install huggingface_hub
-
-# Download entire dataset
-huggingface-cli download YOUR_USERNAME/VCBench --repo-type dataset --local-dir ./vcbench_videos
+huggingface-cli download buaaplay/VCBench --repo-type dataset --local-dir data/videos
 ```
 
-## Annotations
-
-For complete annotations including questions, query points, and ground truth answers, please refer to the original VCBench repository:
-- Object counting annotations: `object_count_data/*.json`
-- Event counting annotations: `event_counting_data/*.json`
+The `chunkedVideos/` directory contains 4,576 video clips (one per query point), each truncated to the query timestamp.
 
-Each annotation file contains:
-- `id`: Question identifier
-- `source_dataset`: Original video source
-- `video_path`: Original video filename
-- `question`: Counting question
-- `query_time` or `query_points`: Timestamp(s) for queries
-- `count`: Ground truth answer(s)
+### Evaluation
 
-## Quality Validation
+To compute metrics (GPA, MoC, UDA) on results using the official evaluation scripts:
 
-All videos have been validated for:
-- ✓ Duration accuracy (100% within ±0.1s)
-- ✓ Frame rate preservation (original fps maintained)
-- ✓ No frame drops or speed changes
-- ✓ Lossless clipping (no re-encoding artifacts)
+```bash
+# Compute metrics on provided results
+python eval/compute_metrics.py results/vcbench_gemini3flash_unified.jsonl data/vcbench_eval.jsonl
+```
 
 ## Citation
 
-If you use this dataset, please cite the VCBench paper:
-
 ```bibtex
-@article{vcbench2026,
-  title={VCBench: A Streaming Counting Benchmark for Spatial-Temporal State Maintenance},
-  author={[Authors]},
-  journal={[Journal/Conference]},
+@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
 
-MIT License - See LICENSE file for details.
-
-## Dataset Statistics
-
-| Category | Clips | Avg Duration | Total Size |
-|----------|-------|--------------|------------|
-| O1-Snap | 252 | ~2min | ~4.3 GB |
-| O1-Delta | 98 | ~1min | ~1.7 GB |
-| O2-Unique | 1,869 | ~3min | ~32 GB |
-| O2-Gain | 78 | ~1min | ~1.3 GB |
-| E1-Action | 1,281 | ~4min | ~28 GB |
-| E1-Transit | 205 | ~2min | ~3.5 GB |
-| E2-Periodic | 280 | ~3min | ~8.7 GB |
-| E2-Episode | 511 | ~2min | ~4.8 GB |
-| **Total** | **4,574** | - | **~80 GB** |
-
-## Contact
-
-For questions or issues, please open an issue in the dataset repository.
+This dataset and code are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).