SAM4MLLM: Enhance Multi-Modal Large Language Model for Referring Expression Segmentation

• URL: http://arxiv.org/abs/2409.10542v1
• Date: Sun, 1 Sep 2024 12:09:33 GMT
• Title: SAM4MLLM: Enhance Multi-Modal Large Language Model for Referring Expression Segmentation
• Authors: Yi-Chia Chen, Wei-Hua Li, Cheng Sun, Yu-Chiang Frank Wang, Chu-Song Chen,
• Abstract summary: We introduce SAM4MLLM, an innovative approach which integrates the Segment Anything Model (SAM) with Multi-Modal Large Language Models (MLLMs) Our method enables MLLMs to learn pixel-level location information without requiring excessive modifications to the existing model architecture or adding specialized tokens. It combines detailed visual information with the powerful expressive capabilities of large language models in a unified language-based manner without additional computational overhead in learning.
• Score: 37.45387861441091
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce SAM4MLLM, an innovative approach which integrates the Segment Anything Model (SAM) with Multi-Modal Large Language Models (MLLMs) for pixel-aware tasks. Our method enables MLLMs to learn pixel-level location information without requiring excessive modifications to the existing model architecture or adding specialized tokens. We introduce an inquiry-based approach that can effectively find prompt points for SAM to perform segmentation based on MLLM. It combines detailed visual information with the powerful expressive capabilities of large language models in a unified language-based manner without additional computational overhead in learning. Experimental results on pubic benchmarks demonstrate the effectiveness of our approach.

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