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KunChang Li?1,4 , Yinan He?1 , Yi Wang??1 , Yizhuo Li1,3 , Wenhai Wang1
Ping Luo3 , Yali Wang4,1 , Limin Wang2,1 , Yu Qiao1
1OpenGVLab, Shanghai AI Laboratory 2Nanjing University 3The University of Hong Kong 4Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
https://github.com/OpenGVLab/Ask-Anything
Abstract
In this study, we initiate an exploration into video understanding by introducing VideoChat, an end-to-end chat-centric video understanding system. It integrates video foundation models and large language models via a learnable neural interface, excelling in spatiotemporal reasoning, event localization, and causal relationship inference. To instructively tune this system, we propose a video-centric instruction dataset, composed of thousands of videos matched with detailed descriptions and conversations. This dataset emphasizes spatiotemporal reasoning and causal relationships, providing a valuable asset for training chat-centric video understanding systems. Preliminary qualitative experiments reveal our system’s potential across a broad spectrum of video applications and set the standard for future research. Access our code and data at https://github.com/OpenGVLab/Ask-Anything.