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README
VideoITG-8B
[🌐Homepage] [💻GitHub] [📜Tech Report] [🤗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:
@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
Contributors
5 commits
Similar repos
Linked in READMEs
10
stars
5
commits
3
linked in READMEs
Aug 13, 2025
updated
README
VideoITG-8B
[🌐Homepage] [💻GitHub] [📜Tech Report] [🤗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:
@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
Similar repos
Linked in READMEs
Contributors
5 commits