From the 1 of 19 linked papers with an AI index.
19 papers
VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding
Xinhao Li, Yuhan Zhu, Xiangyu Zeng +24
VideoChat3 is a fully open, 4B-parameter video-centric multimodal large language model that combines an efficient Inflated 3D Vision Transformer and adaptive frame resolution with…
InternVideo-Next: Towards General Video Foundation Models without Video-Text Supervision
Chenting Wang, Yuhan Zhu, Yicheng Xu +6
Large-scale video-text pretraining achieves strong performance but depends on noisy, synthetic captions with limited semantic coverage, often overlooking implicit world knowledge s…
VideoChat-A1: Thinking with Long Videos by Chain-of-Shot Reasoning
Zikang Wang, Boyu Chen, Zhengrong Yue +4
Recent advances in video understanding have been driven by MLLMs. But these MLLMs are good at analyzing short videos, while suffering from difficulties in understanding videos with…
Super Encoding Network: Recursive Association of Multi-Modal Encoders for Video Understanding
Boyu Chen, Siran Chen, Kunchang Li +3
Video understanding has been considered as one critical step towards world modeling, which is an important long-term problem in AI research. Recently, multimodal foundation models…
VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning
Xinhao Li, Ziang Yan, Desen Meng +7
Reinforcement Learning (RL) benefits Large Language Models (LLMs) for complex reasoning. Inspired by this, we explore integrating spatio-temporal specific rewards into Multimodal L…
A Renaissance of Explicit Motion Information Mining from Transformers for Action Recognition
Peiqin Zhuang, Lei Bai, Yichao Wu +4
Recently, action recognition has been dominated by transformer-based methods, thanks to their spatiotemporal contextual aggregation capacities. However, despite the significant pro…