1 citations · 3 across the 14 of their papers we have counts for
6 papers · 2 filters
VideoChat-M1: Collaborative Policy Planning for Video Understanding via Multi-Agent Reinforcement Learning
Boyu Chen, Zikang Wang, Zhengrong Yue +9
By leveraging tool-augmented Multimodal Large Language Models (MLLMs), multi-agent frameworks are driving progress in video understanding. However, most of them adopt static and no…
UniFlow: A Unified Pixel Flow Tokenizer for Visual Understanding and Generation
Zhengrong Yue, Haiyu Zhang, Xiangyu Zeng +7
Tokenizer is a crucial component for both visual understanding and generation. To advance toward the ultimate goal of universal modeling, recent research has focused on developing…
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-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…
Unveiling Hidden Vulnerabilities in Digital Human Generation via Adversarial Attacks
Zhiying Li, Yeying Jin, Fan Shen +9
Expressive human pose and shape estimation (EHPS) is crucial for digital human generation, especially in applications like live streaming. While existing research primarily focuses…
LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents
Boyu Chen, Zhengrong Yue, Siran Chen +4
Existing MLLMs encounter significant challenges in modeling the temporal context within long videos. Currently, mainstream Agent-based methods use external tools to assist a single…