Unlocking the Potential of MLLMs in Referring Expression Segmentation via a Light-weight Mask Decode
Jingchao Wang, Zhijian Wu, Dingjiang Huang, Yefeng Zheng, Hong Wang
2025-08-08
Summary
This paper talks about MLLMSeg, a model that combines features from a vision encoder and a large language model with a lightweight mask decoder to accurately segment objects people refer to in images.
What's the problem?
The problem is that current multimodal models struggle to precisely separate and identify specific parts of images based on natural language descriptions, often requiring a lot of computing power.
What's the solution?
The solution was to integrate the visual understanding from a multimodal large language model with a specially designed lightweight mask decoder that can quickly and effectively focus on the right parts of images to improve segmentation accuracy and reduce computational costs.
Why it matters?
This matters because it allows AI systems to understand and interact with images better in tasks like photo editing, virtual assistants, and robotics, making these technologies more efficient and responsive.
Abstract
MLLMSeg integrates MLLM vision encoder and LLM features with a lightweight mask decoder to achieve high accuracy in reference expression segmentation with reduced computational cost.