Existing deep video models are limited by specific tasks, fixed input-output
spaces, and poor generalization capabilities, making it difficult to deploy
them in real-world scenarios. In this paper, we present our vision for
multimodal and versatile video understanding and propose a prototype system,
\system. Our system is built upon a tracklet-centric paradigm, which treats
tracklets as the basic video unit and employs various Video Foundation Models
(ViFMs) to annotate their properties e.g., appearance, motion, \etc. All the
detected tracklets are stored in a database and interact with the user through
a database manager. We have conducted extensive case studies on different types
of in-the-wild videos, which demonstrates the effectiveness of our method in
answering various video-related problems. Our project is available at
this https URL

本文提出了一种基于轨迹为中心的多模态视频理解原型系统，使用各种视频基础模型（ViFMs）注释其特性，存储在数据库中，并通过数据库管理器与用户交互，以解决各种视频相关问题。