{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BRLIO34TQ6QT7QXZCHFQCXTFIF","short_pith_number":"pith:BRLIO34T","schema_version":"1.0","canonical_sha256":"0c56876f9387a13fc2f911cb015e6541691a08dfe5d7d5376b74037896f3b0d0","source":{"kind":"arxiv","id":"2502.01216","version":2},"attestation_state":"computed","paper":{"title":"Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Li, Qiuying Li, Tongkun Liu, Xiang Wei, Xiao Jin, Yupeng Shi","submitted_at":"2025-02-03T10:13:34Z","abstract_excerpt":"Industrial defect segmentation is critical for manufacturing quality control. Due to the scarcity of training defect samples, few-shot semantic segmentation (FSS) holds significant value in this field. However, existing studies mostly apply FSS to tackle defects on simple textures, without considering more diverse scenarios. This paper aims to address this gap by exploring FSS in broader industrial products with various defect types. To this end, we contribute a new real-world dataset and reorganize some existing datasets to build a more comprehensive few-shot defect segmentation (FDS) benchma"},"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":"2502.01216","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-03T10:13:34Z","cross_cats_sorted":[],"title_canon_sha256":"2ec7d1666bd3937dd049851458c3039933dff1e9012401e1cd76f6fc4221ea76","abstract_canon_sha256":"01f9feae23b785f6eee721274d2e631c275714f0ae5f3dc317ba67bf1cb74fd7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:12:26.237809Z","signature_b64":"kussU/ABIuPOAqoa+uBlqeHw1nSCNM8uBxtWvJtVw0P8xfy3y5pIybm9sJjbX26hhbNWMOoFrgOJlQJy2J9RDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c56876f9387a13fc2f911cb015e6541691a08dfe5d7d5376b74037896f3b0d0","last_reissued_at":"2026-07-05T10:12:26.237325Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:12:26.237325Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Li, Qiuying Li, Tongkun Liu, Xiang Wei, Xiao Jin, Yupeng Shi","submitted_at":"2025-02-03T10:13:34Z","abstract_excerpt":"Industrial defect segmentation is critical for manufacturing quality control. Due to the scarcity of training defect samples, few-shot semantic segmentation (FSS) holds significant value in this field. However, existing studies mostly apply FSS to tackle defects on simple textures, without considering more diverse scenarios. This paper aims to address this gap by exploring FSS in broader industrial products with various defect types. To this end, we contribute a new real-world dataset and reorganize some existing datasets to build a more comprehensive few-shot defect segmentation (FDS) benchma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01216","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.01216/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2502.01216","created_at":"2026-07-05T10:12:26.237386+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.01216v2","created_at":"2026-07-05T10:12:26.237386+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01216","created_at":"2026-07-05T10:12:26.237386+00:00"},{"alias_kind":"pith_short_12","alias_value":"BRLIO34TQ6QT","created_at":"2026-07-05T10:12:26.237386+00:00"},{"alias_kind":"pith_short_16","alias_value":"BRLIO34TQ6QT7QXZ","created_at":"2026-07-05T10:12:26.237386+00:00"},{"alias_kind":"pith_short_8","alias_value":"BRLIO34T","created_at":"2026-07-05T10:12:26.237386+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.02905","citing_title":"UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BRLIO34TQ6QT7QXZCHFQCXTFIF","json":"https://pith.science/pith/BRLIO34TQ6QT7QXZCHFQCXTFIF.json","graph_json":"https://pith.science/api/pith-number/BRLIO34TQ6QT7QXZCHFQCXTFIF/graph.json","events_json":"https://pith.science/api/pith-number/BRLIO34TQ6QT7QXZCHFQCXTFIF/events.json","paper":"https://pith.science/paper/BRLIO34T"},"agent_actions":{"view_html":"https://pith.science/pith/BRLIO34TQ6QT7QXZCHFQCXTFIF","download_json":"https://pith.science/pith/BRLIO34TQ6QT7QXZCHFQCXTFIF.json","view_paper":"https://pith.science/paper/BRLIO34T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.01216&json=true","fetch_graph":"https://pith.science/api/pith-number/BRLIO34TQ6QT7QXZCHFQCXTFIF/graph.json","fetch_events":"https://pith.science/api/pith-number/BRLIO34TQ6QT7QXZCHFQCXTFIF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BRLIO34TQ6QT7QXZCHFQCXTFIF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BRLIO34TQ6QT7QXZCHFQCXTFIF/action/storage_attestation","attest_author":"https://pith.science/pith/BRLIO34TQ6QT7QXZCHFQCXTFIF/action/author_attestation","sign_citation":"https://pith.science/pith/BRLIO34TQ6QT7QXZCHFQCXTFIF/action/citation_signature","submit_replication":"https://pith.science/pith/BRLIO34TQ6QT7QXZCHFQCXTFIF/action/replication_record"}},"created_at":"2026-07-05T10:12:26.237386+00:00","updated_at":"2026-07-05T10:12:26.237386+00:00"}