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

• URL: http://arxiv.org/abs/2306.05424v2
• Date: Mon, 10 Jun 2024 01:36:53 GMT
• Title: Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
• Authors: Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan,
• Abstract summary: Video-ChatGPT is a multimodal model that merges a video-adapted visual encoder with an LLM. It 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.
• Score: 59.525108086957296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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