LM4LV: A Frozen Large Language Model for Low-level Vision Tasks

• URL: http://arxiv.org/abs/2405.15734v2
• Date: Tue, 11 Jun 2024 15:42:29 GMT
• Title: LM4LV: A Frozen Large Language Model for Low-level Vision Tasks
• Authors: Boyang Zheng, Jinjin Gu, Shijun Li, Chao Dong,
• Abstract summary: $textbfLM4LV$ is a framework that enables a large language model to solve a range of low-level vision tasks without any multi-modal data or prior. This showcases the LLM's strong potential in low-level vision and bridges the gap between MLLMs and low-level vision tasks.
• Score: 25.3601306724822
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The success of large language models (LLMs) has fostered a new research trend of multi-modality large language models (MLLMs), which changes the paradigm of various fields in computer vision. Though MLLMs have shown promising results in numerous high-level vision and vision-language tasks such as VQA and text-to-image, no works have demonstrated how low-level vision tasks can benefit from MLLMs. We find that most current MLLMs are blind to low-level features due to their design of vision modules, thus are inherently incapable for solving low-level vision tasks. In this work, we purpose $\textbf{LM4LV}$, a framework that enables a FROZEN LLM to solve a range of low-level vision tasks without any multi-modal data or prior. This showcases the LLM's strong potential in low-level vision and bridges the gap between MLLMs and low-level vision tasks. We hope this work can inspire new perspectives on LLMs and deeper understanding of their mechanisms. Code is available at https://github.com/bytetriper/LM4LV.

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