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--- |
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license: other |
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license_name: nvlicense |
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license_link: LICENSE |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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base_model: |
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- Qwen/Qwen2-7B-Instruct |
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- google/siglip-so400m-patch14-384 |
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base_model_relation: merge |
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language: |
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- multilingual |
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tags: |
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- VideoITG |
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- Eagle |
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- VLM |
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--- |
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# VideoITG-8B |
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[\[🌐Homepage\]](https://nvlabs.github.io/VideoITG/) [\[💻GitHub\]](https://github.com/NVlabs/VideoITG) [\[📜Tech Report\]](https://arxiv.org/abs/2507.13353) |
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[\[🤗VideoITG-40K\]](https://huggingface.co/datasets/NVEagle/VideoITG-40K) |
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## Introduction |
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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. |
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## Model Details |
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- **Model name**: VideoITG-8B |
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- **Architecture**: Customized Eagle-8B base model, fine-tuned with Instructed Temporal Grounding |
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- **Model type**: Multimodal Large Language Model with Video Understanding |
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- **Languages**: English (primary), multilingual (partially) |
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## Model Performance |
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| Model | Base Model | Frames | LongVideoBench | MLVU | VideoMME | CG-Bench | |
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|---------------------|-------------------|--------|----------------|------|----------|----------| |
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| VideoITG-7B | InternVL2.5-8B | 32 | 61.9 (+2.9%) | 75.0 (+7.8%) | 67.3 (+4.0%) | 46.7 (+7.0%) | |
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| VideoITG-7B | InternVL2.5-26B | 32 | 63.0 (+1.0%) | 78.9 (+6.1%) | 69.9 (+2.5%) | 48.7 (+6.0%) | |
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| VideoITG-7B | LLaVA-Video-7B | 32 | 61.6 (+3.6%) | 74.6 (+8.6%) | 66.1 (+3.0%) | 42.8 (+9.0%) | |
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| VideoITG-7B | LLaVA-Video-7B | 64 | 60.9 (+7.4%) | 76.3 (+7.6%) | 66.4 (+1.9%) | 42.9 (+8.1%) | |
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## Key Features |
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- **Instructed Temporal Grounding**: Intelligently selects video frames based on user instructions |
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- **Plug-and-Play**: Seamlessly integrates with existing video language models |
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- **Superior Temporal Understanding**: Excels in tasks requiring precise temporal grounding |
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## License |
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- Code: [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0) |
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- Model: [NVIDIA License](LICENSE) - Research preview for non-commercial use only |
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## Citation |
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If you find this project useful, please cite our work: |
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```bibtex |
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@article{wang2025videoitg, |
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title = {VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding}, |
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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}, |
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journal = {arXiv preprint arXiv:2507.13353}, |
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year = {2025} |
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} |
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``` |
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## Acknowledgement |
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- [Eagle](https://github.com/NVlabs/EAGLE): The codebase we built upon |
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- [LMMs-Eval](https://github.com/EvolvingLMMs-Lab/lmms-eval): Many thanks to the LMMs-Lab for the easy-to-use evaluation tools |
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- [LLaVA-OneVision](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data) and [LLaVA-Video](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K): We train our models with data from these great open-source projects |
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