{"ID":2854245,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.15849","arxiv_id":"2510.15849","title":"Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt","abstract":"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.","short_abstract":"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 de...","url_abs":"https://arxiv.org/abs/2510.15849","url_pdf":"https://arxiv.org/pdf/2510.15849v2","authors":"[\"Joongwon Chae\",\"Lihui Luo\",\"Xi Yuan\",\"Dongmei Yu\",\"Zhenglin Chen\",\"Lian Zhang\",\"Peiwu Qin\"]","published":"2025-10-17T17:42:28Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":608128,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2854245,"paper_url":"https://arxiv.org/abs/2510.15849","paper_title":"Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt","repo_url":"https://github.com/jw-chae/memory-sam","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}