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nvidia/VideoITG-8B

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VideoITG-8B

[\[🌐Homepage\]](https://nvlabs.github.io/VideoITG/) [\[💻GitHub\]](https://github.com/NVlabs/VideoITG) [\[📜Tech Report\]](https://arxiv.org/abs/2507.13353) [\[🤗VideoITG-40K\]](https://huggingface.co/datasets/NVEagle/VideoITG-40K)

Introduction

VideoITG-8B is a multimodal video understanding model trained with instructed temporal grounding, equipped with the ability to enhance Video Large Language Models through intelligent frame selection. The model tackles the complexities of real-world video scenarios by aligning frame sampling with user instructions. Please check our paper for more details.

Model Details

—Model name: VideoITG-8B
—Architecture: Customized Eagle-8B base model, fine-tuned with Instructed Temporal Grounding
—Model type: Multimodal Large Language Model with Video Understanding
—Languages: English (primary), multilingual (partially)

Model Performance

Model	Base Model	Frames	LongVideoBench	MLVU	VideoMME	CG-Bench
VideoITG-7B	InternVL2.5-8B	32	61.9 (+2.9%)	75.0 (+7.8%)	67.3 (+4.0%)	46.7 (+7.0%)
VideoITG-7B	InternVL2.5-26B	32	63.0 (+1.0%)	78.9 (+6.1%)	69.9 (+2.5%)	48.7 (+6.0%)
VideoITG-7B	LLaVA-Video-7B	32	61.6 (+3.6%)	74.6 (+8.6%)	66.1 (+3.0%)	42.8 (+9.0%)
VideoITG-7B	LLaVA-Video-7B	64	60.9 (+7.4%)	76.3 (+7.6%)	66.4 (+1.9%)	42.9 (+8.1%)

Key Features

—Instructed Temporal Grounding: Intelligently selects video frames based on user instructions
—Plug-and-Play: Seamlessly integrates with existing video language models
—Superior Temporal Understanding: Excels in tasks requiring precise temporal grounding

License

—Code: Apache 2.0 License
—Model: NVIDIA License - Research preview for non-commercial use only

Citation

If you find this project useful, please cite our work:

bibtex

@article{wang2025videoitg,
  title     = {VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding},
  author    = {Shihao Wang and Guo Chen and De-An Huang and Zhiqi Li and Minghan Li and Guilin Liu and Jose M. Alvarez and Lei Zhang and Zhiding Yu},
  journal   = {arXiv preprint arXiv:2507.13353},
  year      = {2025}
}

Acknowledgement

—Eagle: The codebase we built upon
—LMMs-Eval: Many thanks to the LMMs-Lab for the easy-to-use evaluation tools
—LLaVA-OneVision and LLaVA-Video: We train our models with data from these great open-source projects