SVBench
Full name: Streaming Video Understanding Benchmark

# **Dataset Card for SVBench**

This dataset card aims to provide a comprehensive overview of the SVBench dataset, including its purpose, structure, and sources. For details, see our [Project](https://yzy-bupt.github.io/SVBench/), [Paper](https://arxiv.org/abs/2502.10810) and [GitHub repository](https://github.com/yzy-bupt/SVBench).

## **Dataset Details**

### **Dataset Description**

SVBench is the first benchmark specifically designed to evaluate **long-context streaming video understanding** through **temporal multi-turn question-answering (QA) chains**. It addresses the limitations of existing video understanding benchmarks by emphasizing **continuous temporal reasoning** over streaming video data.

The dataset includes:

- **1,353 streaming videos** (average duration: 1–8 minutes)
- **49,979 QA pairs** organized into **multi-turn dialogues**
- **Temporal linkages** between QA chains to simulate real-world streaming scenarios

**Languages**

- **NLP**: English & Chinese (bilingual annotations)

**License**

- Apache-2.0

**Dataset Sources**

- [YT-Temporal-1B](https://rowanzellers.com/merlotreserve/#data)|[YouCook2](http://youcook2.eecs.umich.edu/download)|[MovieChat](https://huggingface.co/datasets/Enxin/MovieChat-1K-test/tree/main)|[Panda-70M](https://snap-research.github.io/Panda-70M/)|[Ego4D](https://ego4d-data.org/#download)|[ActivityNet](http://activity-net.org/download.html).

## **Uses**

Download SVBench dataset from Hugging Face:



```
git clone https://huggingface.co/yzy666/SVBench
```



**Intended Use**

- Evaluate **long-context reasoning** capabilities of Large Vision-Language Models (LVLMs) in streaming video scenarios.
- Benchmark **temporal understanding** through multi-turn dialogues.
- Support research in **streaming video QA**, **activity forecasting**, and **interactive AI assistants**.

**Direct Use**

- Training or fine-tuning LVLMs for streaming video tasks.
- Testing model robustness in handling **dynamic, time-sensitive queries**.
- Comparative analysis of open-source vs. closed-source LVLMs.

**Restrictions**

- Videos and annotations are for **research purposes only**.
- Users must comply with the original licenses of datasets.

## **Dataset Structure**

- **Folder Structure Tree:**

```
SVBench/
├── Con/
│   ├── Con_EN/
│   └── Con_ZH/
├── Dialogue/
│   ├── Dialogue_EN/
│   └── Dialogue_ZH/
├── Meta/
│   ├── Meta_EN/
│   └── Meta_ZH/
├── Path/
├── Src/
├── Streaming/
│   ├── Streaming_EN/
│   └── Streaming_ZH/
├── Video/
└── Your_Model_Name/
    ├── dialogue/
    │   └── --NDulaHyrE.json
    └── streaming/
        └── -4h8cuweoKo.json
```

- **Dataset Division:** 

- - Training Set: 42,605 QA pairs and 1,153 videos
  - Testing Set: 7,374 QA pairs and 200 videos

## **Data Fields**

| **Key**                   | **Description**                                              |
| ------------------------- | ------------------------------------------------------------ |
| Video_Name                | Unique identifier or title of the video file.                |
| Sort_of_Set               | Category or type of the dataset subset (e.g., "Train", "Test"). |
| Path_of_QandA             | File path to the question-answer pairs.                      |
| Path_of_Con               | Path to relationship files.                                  |
| Path_of_StreamingPathData | Path to Q&A sequence for streaming evaluation. Each streaming path contains all Q&A sequences in streaming order within the path. |
| Path_of_Dialogue          | Path to Q&A sequence for dialogue evaluation. Each dialogue contains all Q&A sequences in order within the dialogue. |
| Path_of_Streaming         | Path to Q&A sequence for streaming evaluation represented only by serial numbers (e.g., the path [[0,0],[1,2],[2,3]...] indicates starting from the 1st question of the 1st chain, then proceeding to the 3rd question of the 2nd chain, and then to the 4th question of the 3rd chain, and so on). |
| Path_of_Video             | Absolute file path to the raw video file.                    |
| Video_Duration            | Total duration of the video in seconds.                      |
| Source_of_Dataset         | Origin of the dataset.                                       |

## **Leaderboard Submission**

Submit results via https://forms.gle/Rmi6u4WGhyEZ2X7g8.

**Submission Instructions:**

1. Save result files following the case structure under the SVBench/[Your_Model_Name] directory
2. Compress the entire [Your_Model_Name] folder into a ZIP archive
3. Upload the generated ZIP file through this submission portal

Important Notes:

- Maintain the exact directory hierarchy: [Your_Model_Name]/[dialogue/streaming]/[Video_Name.json]
- Ensure your model name follows exact capitalization (case-sensitive)
- Package only the required result files (avoid including source code/executables)

**Submission Form**

![img](https://s2.loli.net/2025/03/16/AbhS7nB9Raf1uvk.png)

## **Dataset Annotation**

See [GitHub repository](https://github.com/yzy-bupt/SVBench) for details.

Semi-automated annotation using a hybrid approach:

- Automatic QA Generation: Leveraging GPT-4 to generate dialogue chains based on video transcripts.
- Human Verification: Annotators validate temporal links and refine QA pairs for consistency.
- Temporal Linking: Artificial alignment of QA chains across video segments.
- Quality Control: Inter-annotator agreement >90% for temporal links.

## **Citation**

If you find our data useful, please consider citing our work!

```
@article{yang2025svbench,
  title={SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding},
  author={Yang, Zhenyu and Hu, Yuhang and Du, Zemin and Xue, Dizhan and Qian, Shengsheng and Wu, Jiahong and Yang, Fan and Dong, Weiming and Xu, Changsheng},
  journal={arXiv preprint arXiv:2502.10810},
  year={2025}
}
```

Introducing paper: SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding