Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt
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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_ | 1866918501345656832 |
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| author | Chae, Joongwon Luo, Lihui Yuan, Xi Yu, Dongmei Chen, Zhenglin Zhang, Lian Qin, Peiwu |
| author_facet | Chae, Joongwon Luo, Lihui Yuan, Xi Yu, Dongmei Chen, Zhenglin Zhang, Lian Qin, Peiwu |
| contents | Accurate tongue segmentation is crucial for reliable TCM analysis. Supervised models require large annotated datasets, while SAM-family models remain prompt-driven. We present Memory-SAM, a training-free, human-prompt-free pipeline that automatically generates effective prompts from a small memory of prior cases via dense DINOv3 features and FAISS retrieval. Given a query image, mask-constrained correspondences to the retrieved exemplar are distilled into foreground/background point prompts that guide SAM2 without manual clicks or model fine-tuning. We evaluate on 600 expert-annotated images (300 controlled, 300 in-the-wild). On the mixed test split, Memory-SAM achieves mIoU 0.9863, surpassing FCN (0.8188) and a detector-to-box SAM baseline (0.1839). On controlled data, ceiling effects above 0.98 make small differences less meaningful given annotation variability, while our method shows clear gains under real-world conditions. Results indicate that retrieval-to-prompt enables data-efficient, robust segmentation of irregular boundaries in tongue imaging. The code is publicly available at https://github.com/jw-chae/memory-sam. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_15849 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt Chae, Joongwon Luo, Lihui Yuan, Xi Yu, Dongmei Chen, Zhenglin Zhang, Lian Qin, Peiwu Computer Vision and Pattern Recognition Accurate tongue segmentation is crucial for reliable TCM analysis. Supervised models require large annotated datasets, while SAM-family models remain prompt-driven. We present Memory-SAM, a training-free, human-prompt-free pipeline that automatically generates effective prompts from a small memory of prior cases via dense DINOv3 features and FAISS retrieval. Given a query image, mask-constrained correspondences to the retrieved exemplar are distilled into foreground/background point prompts that guide SAM2 without manual clicks or model fine-tuning. We evaluate on 600 expert-annotated images (300 controlled, 300 in-the-wild). On the mixed test split, Memory-SAM achieves mIoU 0.9863, surpassing FCN (0.8188) and a detector-to-box SAM baseline (0.1839). On controlled data, ceiling effects above 0.98 make small differences less meaningful given annotation variability, while our method shows clear gains under real-world conditions. Results indicate that retrieval-to-prompt enables data-efficient, robust segmentation of irregular boundaries in tongue imaging. The code is publicly available at https://github.com/jw-chae/memory-sam. |
| title | Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.15849 |