TencentARC/TimeLens-100K

Dataset

7

stars

5

commits

5

linked in READMEs

Dec 19, 2025

updated

README

TimeLens-100K

πŸ“‘ Paper | πŸ’» Code | 🏠 Project Page | πŸ€— Model & Data

✨ Dataset Description

TimeLens-100K is a large-scale, diverse, and high-quality training dataset for video temporal grounding. It was proposed in our paper TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs and used for training TimeLens models. The annotation process was conducted using an automated pipeline powered by Gemini-2.5-Pro.

πŸ“Š Dataset Statistics

πŸš€ Usage

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

πŸ“ 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}
}

Contributors

JungleGym

5 commits

TencentARC/TimeLens-100K

Dataset

7

stars

5

commits

5

linked in READMEs

Dec 19, 2025

updated

README

TimeLens-100K

πŸ“‘ Paper | πŸ’» Code | 🏠 Project Page | πŸ€— Model & Data

✨ Dataset Description

TimeLens-100K is a large-scale, diverse, and high-quality training dataset for video temporal grounding. It was proposed in our paper TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs and used for training TimeLens models. The annotation process was conducted using an automated pipeline powered by Gemini-2.5-Pro.

πŸ“Š Dataset Statistics

πŸš€ Usage

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

πŸ“ 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}
}

Contributors

JungleGym

5 commits