补充雷达 · 非 Top 3展开内容不代表入选
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 generalist video-centric MLLM. VideoChat3 advances video understanding through two complementary designs. For efficiency, we introduce Inflated 3D Vision Transformer (I3D-ViT) and Adaptive Frame Resolution for Streaming Video Perception, which enables efficient spatiotemporal representation and reduces the cost of processing video inputs during training and inference. For effectiveness, we develop a scalable video data synthesis pipeline that curates three diverse, high-quality training datasets: VideoChat3-Academic2M, VideoChat3-LV116K, and VideoChat3-OL617K, covering general, long-form, and streaming video scenarios, improving the model's generalization across domains. By integrating these designs, VideoChat3 achieves a rare balance of broad generalization and computational efficiency. Experiments across general, long-form, and streaming benchmarks demonstrate that VideoChat3 surpasses prior
关注理由当日社区关注度较高，值得进一步阅读原文。
研究问题从摘要看，论文关注 AI 模型能力、训练或应用中的具体瓶颈。
核心方法摘要未完整披露方法细节，建议打开论文核验模型、数据和训练设置。
关键点- 保留 Hugging Face、arXiv 与辅助学术信号，方便回溯来源。
- 优先阅读摘要、实验设置和结果表，确认是否值得公众号展开。
- 若存在代码仓库或 benchmark，可作为工程落地判断依据。
局限与风险来源摘要未披露，发布前需人工复核实验范围和失败案例。
公众号角度多模态方向的当天热点论文，可结合方法与实验做选题。
HF 120 赞arXiv cs.CV代码仓库可见S2 引用 0