VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding
Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current open-source models remain limited in several ways. They often struggle to generalize across diverse video types, making them effective only in specific domains. High computational demands further restrict their efficiency and scalability. Moreover, most models are only partially open, with key components such as training code, strategy, or datasets unavailable, which hinders reproducibility and slows community-driven development. To address these issues, we introduce VideoChat3, a fully open, efficient, and gen...
| Authors | Xinhao Li, Yuhan Zhu, Xiangyu Zeng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 July 2026 |
| Last updated at source | 23 August 2026 |
Connected in the knowledge graph
- MCG-NJU/VideoChat3-OL617kDataset · cited by
- MCG-NJU/VideoChat3-4BModel · cited by
- MCG-NJU/VideoChat3-LV116kDataset · cited by
- MCG-NJU/VideoChat3-Academic2MDataset · cited by
- youness6sm/SamaaDataset · cited by
- lmwang/VideoChat3-Stage3-Training-DataDataset · cited by
- rookierufus/Video_VJEPA_CMPR_ADJDataset · cited by
- MCG-NJU/VideoChat3-Training-Data-AnnotationsDataset · cited by
Models and datasets referencing this paper
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.