VideoChat: Chat-Centric Video Understanding

Autor:	Li, KunChang, He, Yinan, Wang, Yi, Li, Yizhuo, Wang, Wenhai, Luo, Ping, Wang, Yali, Wang, Limin, Qiao, Yu
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	In this paper, we initiate an attempt of developing an end-to-end chat-centric video understanding system, coined as VideoChat. 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 build a video-centric instruction dataset, composed of thousands of videos associated with detailed descriptions and conversations. This dataset emphasizes spatiotemporal reasoning and captures causal relationships, providing a valuable asset for training our chat-centric video understanding system. Preliminary qualitative experiments demonstrate the potential of our system across a broad spectrum of video applications, which could serve as a simple prototype system for future research on chat-centric video understanding. Access our code and data at https://github.com/OpenGVLab/Ask-Anything Comment: Technical report
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.06355 Zobrazit plný text záznamu View this record from Arxiv