CMaP-SAM: Contraction Mapping Prior for SAM-driven Few-shot Segmentation

• URL: http://arxiv.org/abs/2504.05049v1
• Date: Mon, 07 Apr 2025 13:19:16 GMT
• Title: CMaP-SAM: Contraction Mapping Prior for SAM-driven Few-shot Segmentation
• Authors: Shuai Chen, Fanman Meng, Haoran Wei, Chenhao Wu, Qingbo Wu, Linfeng Xu, Hongliang Li,
• Abstract summary: Few-shot segmentation (FSS) aims to segment new classes using few annotated images.<n>Recent FSS methods have shown considerable improvements by leveraging Segment Anything Model (SAM)<n>We propose CMaP-SAM, a novel framework that introduces contraction mapping theory to optimize position priors for SAM-driven FSS.
• Score: 21.466035540502226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot segmentation (FSS) aims to segment new classes using few annotated images. While recent FSS methods have shown considerable improvements by leveraging Segment Anything Model (SAM), they face two critical limitations: insufficient utilization of structural correlations in query images, and significant information loss when converting continuous position priors to discrete point prompts. To address these challenges, we propose CMaP-SAM, a novel framework that introduces contraction mapping theory to optimize position priors for SAM-driven few-shot segmentation. CMaP-SAM consists of three key components: (1) a contraction mapping module that formulates position prior optimization as a Banach contraction mapping with convergence guarantees. This module iteratively refines position priors through pixel-wise structural similarity, generating a converged prior that preserves both semantic guidance from reference images and structural correlations in query images; (2) an adaptive distribution alignment module bridging continuous priors with SAM's binary mask prompt encoder; and (3) a foreground-background decoupled refinement architecture producing accurate final segmentation masks. Extensive experiments demonstrate CMaP-SAM's effectiveness, achieving state-of-the-art performance with 71.1 mIoU on PASCAL-$5^i$ and 56.1 on COCO-$20^i$ datasets.

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