CMP: A Composable Meta Prompt for SAM-Based Cross-Domain Few-Shot Segmentation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909699983540224 |
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| author | Chen, Shuai Meng, Fanman Yang, Chunjin Wei, Haoran Wu, Chenhao Wu, Qingbo Li, Hongliang |
| author_facet | Chen, Shuai Meng, Fanman Yang, Chunjin Wei, Haoran Wu, Chenhao Wu, Qingbo Li, Hongliang |
| contents | Cross-Domain Few-Shot Segmentation (CD-FSS) remains challenging due to limited data and domain shifts. Recent foundation models like the Segment Anything Model (SAM) have shown remarkable zero-shot generalization capability in general segmentation tasks, making it a promising solution for few-shot scenarios. However, adapting SAM to CD-FSS faces two critical challenges: reliance on manual prompt and limited cross-domain ability. Therefore, we propose the Composable Meta-Prompt (CMP) framework that introduces three key modules: (i) the Reference Complement and Transformation (RCT) module for semantic expansion, (ii) the Composable Meta-Prompt Generation (CMPG) module for automated meta-prompt synthesis, and (iii) the Frequency-Aware Interaction (FAI) module for domain discrepancy mitigation. Evaluations across four cross-domain datasets demonstrate CMP's state-of-the-art performance, achieving 71.8\% and 74.5\% mIoU in 1-shot and 5-shot scenarios respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16753 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CMP: A Composable Meta Prompt for SAM-Based Cross-Domain Few-Shot Segmentation Chen, Shuai Meng, Fanman Yang, Chunjin Wei, Haoran Wu, Chenhao Wu, Qingbo Li, Hongliang Computer Vision and Pattern Recognition Cross-Domain Few-Shot Segmentation (CD-FSS) remains challenging due to limited data and domain shifts. Recent foundation models like the Segment Anything Model (SAM) have shown remarkable zero-shot generalization capability in general segmentation tasks, making it a promising solution for few-shot scenarios. However, adapting SAM to CD-FSS faces two critical challenges: reliance on manual prompt and limited cross-domain ability. Therefore, we propose the Composable Meta-Prompt (CMP) framework that introduces three key modules: (i) the Reference Complement and Transformation (RCT) module for semantic expansion, (ii) the Composable Meta-Prompt Generation (CMPG) module for automated meta-prompt synthesis, and (iii) the Frequency-Aware Interaction (FAI) module for domain discrepancy mitigation. Evaluations across four cross-domain datasets demonstrate CMP's state-of-the-art performance, achieving 71.8\% and 74.5\% mIoU in 1-shot and 5-shot scenarios respectively. |
| title | CMP: A Composable Meta Prompt for SAM-Based Cross-Domain Few-Shot Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.16753 |