  Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the under-explored
field of \emph{video-based conversation} by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with an LLM. The
resulting model is capable of understanding and generating detailed
conversations about videos. We introduce a new dataset of 100,000
video-instruction pairs used to train Video-ChatGPT acquired via manual and
semi-automated pipeline that is easily scalable and robust to label noise. We
also develop a quantitative evaluation framework for video-based dialogue
models to objectively analyze the strengths and weaknesses of video-based
dialogue models. Code: https://github.com/mbzuai-oryx/Video-ChatGPT.
