[ACL 2026] StreamingEval: A Unified Evaluation Protocol towards Realistic Streaming Video Understanding

Guowei Tang, Tianwen Qian^†, Huanran Zheng, Yifei Wang, Xiaoling Wang

†Corresponding author

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Introduction

Real-time, continuous understanding of visual signals is essential for real-world interactive AI applications, and poses a fundamental system-level challenge. Existing research on streaming video understanding, however, typically focuses on isolated aspects such as question-answering accuracy under limited visual context or improvements in encoding efficiency, while largely overlooking practical deployability under realistic resource constraints. To bridge this gap, we introduce StreamingEval, a unified evaluation framework for assessing the streaming video understanding capabilities of Video-LLMs under realistic constraints.

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Experimental result

Notification

All the code is coming soon.

Citation

If you find this project useful in your research, please consider citing:

@inproceedings{tang2026streamingeval,
  title     = {StreamingEval: A Unified Evaluation Protocol towards Realistic Streaming Video Understanding},
  author    = {Tang, Guowei and Qian, Tianwen and Zheng, Huanran and Wang, Yifei and Wang, Xiaoling},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year      = {2026},
  address   = {San Diego, California, USA},
  publisher = {Association for Computational Linguistics}
}

Acknowledgments

Project Supported by Shanghai General AI Foundation Models Program (Grant No. 2025SHZDZX025G16)

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License

This project is licensed under the Apache-2.0 License.