Summary of Sam4mllm: Enhance Multi-modal Large Language Model For Referring Expression Segmentation, by Yi-chia Chen et al.
SAM4MLLM: Enhance Multi-Modal Large Language Model for Referring Expression Segmentation
by Yi-Chia Chen, Wei-Hua Li, Cheng Sun, Yu-Chiang Frank Wang, Chu-Song Chen
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces SAM4MLLM, an innovative method that integrates the Segment Anything Model (SAM) with Multi-Modal Large Language Models (MLLMs) for pixel-aware tasks. The proposed approach enables MLLMs to learn pixel-level location information without requiring significant changes to the existing model architecture or adding specialized tokens. The authors introduce an inquiry-based approach that effectively finds prompt points for SAM to perform segmentation based on MLLM, combining detailed visual information with the powerful expressive capabilities of large language models in a unified language-based manner. Experimental results on public benchmarks demonstrate the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAM4MLLM is a new way to use big language models to understand images better. It combines two things: a model that can segment images (SAM) and a powerful language model (MLLM). The new method lets MLLMs learn about individual pixels in an image without needing lots of extra changes or special tokens. The authors also came up with a way to find the right starting points for SAM to work on images, combining visual details with language capabilities. This approach was tested and showed it can do a good job. |
Keywords
» Artificial intelligence » Language model » Multi modal » Prompt » Sam
