OmniViD: A Generative Framework for Universal Video Understanding
Authors: Junke Wang, Dongdong Chen, Chong Luo, Bo He, Lu Yuan, Zuxuan Wu, Yu-Gang Jiang

The core of video understanding tasks such as recognition captioning and tracking is to automatically detect objects or actions in a video and analyze their temporal evolution. Despite sharing a common goal different tasks often rely on distinct model architectures and annotation formats. In contrast natural language processing benefits from a unified output space i.e. text sequences which simplifies the training of powerful foundational language models such as GPT-3 with extensive training corpora. Inspired by this we seek to unify the output space of video understanding tasks by using languages as labels and additionally introducing time and box tokens. In this way a variety of video tasks could be formulated as video-grounded token generation. This enables us to address various types of video tasks including classification (such as action recognition) captioning (covering clip captioning video question answering and dense video captioning) and localization tasks (such as visual object tracking) within a fully shared encoder-decoder architecture following a generative framework. Through comprehensive experiments we demonstrate such a simple and straightforward idea is quite effective and can achieve state-of-the-art or competitive results on seven video benchmarks providing a novel perspective for more universal video understanding. Code is available at \href https://github.com/wangjk666/OmniVid  https://github.com/wangjk666/OmniVid .