VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding
Paper
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2507.13353
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Published
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1
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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.
| 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%) |
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}
}