{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TLW74ZPN4LSJISWI72I5NEFG4M","short_pith_number":"pith:TLW74ZPN","schema_version":"1.0","canonical_sha256":"9aedfe65ede2e4944ac8fe91d690a6e33bb7b42d2b047d7217be614a6e0b7ca6","source":{"kind":"arxiv","id":"2510.15849","version":2},"attestation_state":"computed","paper":{"title":"Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dongmei Yu, Joongwon Chae, Lian Zhang, Lihui Luo, Peiwu Qin, Xi Yuan, Zhenglin Chen","submitted_at":"2025-10-17T17:42:28Z","abstract_excerpt":"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 ("},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2510.15849","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-10-17T17:42:28Z","cross_cats_sorted":[],"title_canon_sha256":"8ace2d8c0e8d891b3dcefa830c0346512869a83fc5005fbfbd1730b55e8f4fda","abstract_canon_sha256":"a02e7f3dc8160a54744cd98482766f3b32a17769121a300900709dba86a65193"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:04.693952Z","signature_b64":"OE38sTS9mlNG721inqVTmayDhJK8do1e8yzpA4gT2dyouBWVbjIoe+Tjueranzwu1FNXX3eKN4inKAM5lIT7Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9aedfe65ede2e4944ac8fe91d690a6e33bb7b42d2b047d7217be614a6e0b7ca6","last_reissued_at":"2026-05-17T23:39:04.693139Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:04.693139Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dongmei Yu, Joongwon Chae, Lian Zhang, Lihui Luo, Peiwu Qin, Xi Yuan, Zhenglin Chen","submitted_at":"2025-10-17T17:42:28Z","abstract_excerpt":"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 ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.15849","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2510.15849","created_at":"2026-05-17T23:39:04.693268+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.15849v2","created_at":"2026-05-17T23:39:04.693268+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.15849","created_at":"2026-05-17T23:39:04.693268+00:00"},{"alias_kind":"pith_short_12","alias_value":"TLW74ZPN4LSJ","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"TLW74ZPN4LSJISWI","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"TLW74ZPN","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TLW74ZPN4LSJISWI72I5NEFG4M","json":"https://pith.science/pith/TLW74ZPN4LSJISWI72I5NEFG4M.json","graph_json":"https://pith.science/api/pith-number/TLW74ZPN4LSJISWI72I5NEFG4M/graph.json","events_json":"https://pith.science/api/pith-number/TLW74ZPN4LSJISWI72I5NEFG4M/events.json","paper":"https://pith.science/paper/TLW74ZPN"},"agent_actions":{"view_html":"https://pith.science/pith/TLW74ZPN4LSJISWI72I5NEFG4M","download_json":"https://pith.science/pith/TLW74ZPN4LSJISWI72I5NEFG4M.json","view_paper":"https://pith.science/paper/TLW74ZPN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.15849&json=true","fetch_graph":"https://pith.science/api/pith-number/TLW74ZPN4LSJISWI72I5NEFG4M/graph.json","fetch_events":"https://pith.science/api/pith-number/TLW74ZPN4LSJISWI72I5NEFG4M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TLW74ZPN4LSJISWI72I5NEFG4M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TLW74ZPN4LSJISWI72I5NEFG4M/action/storage_attestation","attest_author":"https://pith.science/pith/TLW74ZPN4LSJISWI72I5NEFG4M/action/author_attestation","sign_citation":"https://pith.science/pith/TLW74ZPN4LSJISWI72I5NEFG4M/action/citation_signature","submit_replication":"https://pith.science/pith/TLW74ZPN4LSJISWI72I5NEFG4M/action/replication_record"}},"created_at":"2026-05-17T23:39:04.693268+00:00","updated_at":"2026-05-17T23:39:04.693268+00:00"}