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

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/).