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TimeLens-Bench

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TimeLens-Bench

📑 Paper | 💻 Code | 🏠 Project Page | 🤗 Model & Data | 🏆 TimeLens-Bench Leaderboard

✨ Dataset Description

TimeLens-Bench is a comprehensive, high-quality evaluation benchmark for video temporal grounding, proposed in our paper TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs.

During our annotation process, we identified critical quality issues within existing datasets and performed extensive manual corrections. We observed a dramatic re-ranking of models on TimeLens-Bench compared to legacy benchmarks, demonstrating that TimeLens-Bench provides more reliable evaluation for video temporal grounding. (See more details in our paper and project page.) performance_comparison_charades-1

📊 Dataset Statistics

The benchmark consists of manually refined versions of three widely used evaluation datasets for video temporal grounding:

Refined Dataset	# Videos	Avg. Duration	# Annotations	Source Dataset	Source Dataset Link
Charades-TimeLens	1313	29.6	3363	Charades-STA	https://github.com/jiyanggao/TALL
ActivityNet-TimeLens	1455*	134.9	4500	ActivityNet-Captions	https://cs.stanford.edu/people/ranjaykrishna/densevid/
QVHighlights-TimeLens	1511	149.6	1541	QVHighlights	https://github.com/jayleicn/moment_detr

* To reduce the high evaluation cost from the excessively large ActivityNet Captions, we sampled videos uniformly across duration bins to curate ActivityNet-TimeLens.

🚀 Usage

To download and use the benchmark for evaluation, please refer to the instructions in our GitHub Repository.

📝 Citation

If you find our work helpful for your research and applications, please cite our paper:

@article{zhang2025timelens,
  title={TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs},
  author={Zhang, Jun and Wang, Teng and Ge, Yuying and Ge, Yixiao and Li, Xinhao and Shan, Ying and Wang, Limin},
  journal={arXiv preprint arXiv:2512.14698},
  year={2025}
}