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Conversation agents fueled by Large Language Models (LLMs) are providing anew way to interact with visual data. While there have been initial attemptsfor image-based conversation models, this work addresses the underexploredfield of video-based conversation by introducing Video-ChatGPT. It is amultimodal model that merges a video-adapted visual encoder with a LLM. Themodel is capable of understanding and generating human-like conversations aboutvideos. We introduce a new dataset of 100,000 video-instruction pairs used totrain Video-ChatGPT acquired via manual and semi-automated pipeline that iseasily scalable and robust to label noise. We also develop a quantiativeevaluation framework for video-based dialogue models to objectively analyse thestrengths and weaknesses of proposed models. Our code, models, instruction-setsand demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.

Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models