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Existing deep video models are limited by specific tasks, fixed input-outputspaces, and poor generalization capabilities, making it difficult to deploythem in real-world scenarios. In this paper, we present our vision formultimodal and versatile video understanding and propose a prototype system,\system. Our system is built upon a tracklet-centric paradigm, which treatstracklets as the basic video unit and employs various Video Foundation Models(ViFMs) to annotate their properties e.g., appearance, motion, \etc. All thedetected tracklets are stored in a database and interact with the user througha database manager. We have conducted extensive case studies on different typesof in-the-wild videos, which demonstrates the effectiveness of our method inanswering various video-related problems. Our project is available athttps://www.wangjunke.info/ChatVideo/

ChatVideo: A Tracklet-centric Multimodal and Versatile Video Understanding System