Title: Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Authors: Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
Published: 8th June 2023 (Thursday) @ 17:59:56
Link: http://arxiv.org/abs/2306.05424v2
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.
Video Instruction Dataset [22] offers a rich resource of 100,000 question-answer pairs distributed across 13,224 videos, distinguished by meticulous annotations. Noteworthy for its high-quality annotations, this dataset presents detailed answers to questions, averaging 57 words per sentence. Spanning diverse question types, including Video Summarization and Description-based QAs, it addresses spatial, temporal, relationship, and reasoning aspects, alongside creative or generative QAs.
â from §4.1. Experiments: Datasets - Training Datasets of MiniGPT4-Video Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens