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ProactiveVideoQA: A Comprehensive Benchmark Evaluating Proactive Interactions in Video Large Language Models
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<div style="margin: 30px 0">
<a href="https://arxiv.org/abs/2507.09313" style="margin: 0 10px">📄 arXiv Paper</a> |
<a href="https://github.com/yellow-binary-tree/ProactiveVideoQA" style="margin: 0 10px"> 🖥️ Github Code </a> |
<a href="https://huggingface.co/datasets/wangyueqian/ProactiveVideoQA" style="margin: 0 10px">📦 Data</a>
</div>
</div>
## Introduction
ProactiveVideoQA is the first comprehensive benchmark designed to evaluate a system's ability to engage in proactive interaction in multimodal dialogue settings.
Unlike traditional turn-by-turn dialogue systems, in proactive intraction model need to determine when to repsond during the playback, so both response timing and response textual content are important points for evaluation.
## Dataset Statistics
ProactiveVideoQA contains 4 tasks:
1. **Proactive web-video QA** `[WEB]`: centering on general web-video understanding.
1. **Proactive ego-centric video QA** `[EGO]`: centering on first-person-view video comprehension, particularly relevant in robotics and daily assistant applications.
1. **Proactive TV-series video QA** `[TV]`: emphasizing dialogue and social relationship understanding with speech input, and
1. **Proactive video anomaly detection** `[VAD]` targeting surveillance video monitoring and alerting.
- **1377** videos from different sources
- **1427** different qeustions, and **3510** ground truth reply turns
- Fully proactive questions and open-ended answers ✅
## Data Format
Each test example in `{dataset}/anno.json` has the following format:
```json
{
"question_id": "OSfMU69X3C4.7.mp4", // unique identifier for this test example
"video": "OSfMU69X3C4.7.mp4", // video file name in `video` folder
"conversation": [ // model input
{"role": "user", "time": 0, "content": "What are the people doing in the office?"}
],
"answer": [ // expected model output
{ // model are expected to reply with the content in the reply timespan
"role": "assistant", "content": "People are working at workstations.",
"reply_timespan": [0.0, 9.88]
},
{ ... }
]
}
```
## Citation
```bibtex
@misc{wang2025proactivevideoqacomprehensivebenchmarkevaluating,
title={ProactiveVideoQA: A Comprehensive Benchmark Evaluating Proactive Interactions in Video Large Language Models},
author={Yueqian Wang and Xiaojun Meng and Yifan Wang and Huishuai Zhang and Dongyan Zhao},
year={2025},
eprint={2507.09313},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2507.09313},
}
```
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