VideoChat: Chat-Centric Video Understanding
Authors: name: KunChang Li, name: Yinan He, name: Yi Wang, name: Yizhuo Li, name: Wenhai Wang, name: Ping Luo, name: Yali Wang, name: Limin Wang, name: Yu Qiao

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