MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action

• URL: http://arxiv.org/abs/2303.11381v1
• Date: Mon, 20 Mar 2023 18:31:47 GMT
• Title: MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action
• Authors: Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, Lijuan Wang
• Abstract summary: MM-REACT is a system paradigm that integrates ChatGPT with a pool of vision experts to achieve multimodal reasoning and action. MM-REACT's prompt design allows language models to accept, associate, and process multimodal information.
• Score: 96.33509740612486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose MM-REACT, a system paradigm that integrates ChatGPT with a pool of vision experts to achieve multimodal reasoning and action. In this paper, we define and explore a comprehensive list of advanced vision tasks that are intriguing to solve, but may exceed the capabilities of existing vision and vision-language models. To achieve such advanced visual intelligence, MM-REACT introduces a textual prompt design that can represent text descriptions, textualized spatial coordinates, and aligned file names for dense visual signals such as images and videos. MM-REACT's prompt design allows language models to accept, associate, and process multimodal information, thereby facilitating the synergetic combination of ChatGPT and various vision experts. Zero-shot experiments demonstrate MM-REACT's effectiveness in addressing the specified capabilities of interests and its wide application in different scenarios that require advanced visual understanding. Furthermore, we discuss and compare MM-REACT's system paradigm with an alternative approach that extends language models for multimodal scenarios through joint finetuning. Code, demo, video, and visualization are available at https://multimodal-react.github.io/

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