Paper · arXiv 2306.05424
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
Video-ChatGPT is a multimodal model combining a video-adapted visual encoder with a Large Language Model to understand and generate human-like conversations about videos, trained on a scalable dataset of video-instruction pairs.
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 underexplored field of video-based conversation by introducing Video-ChatGPT. It is a multimodal model that merges a video-adapted visual encoder with a LLM. The model is capable of understanding and generating human-like 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 quantiative evaluation framework for video-based dialogue models to objectively analyse the strengths and weaknesses of proposed models. Our code, models, instruction-sets and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.