{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AULEMWECPHQE5OYRQK42VH5ZO3","short_pith_number":"pith:AULEMWEC","schema_version":"1.0","canonical_sha256":"051646588279e04ebb1182b9aa9fb976ef8251f0ce4615ae0330534a268be8af","source":{"kind":"arxiv","id":"2406.07333","version":1},"attestation_state":"computed","paper":{"title":"Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haiming Yao, Wei Luo, Weiming Shen, Wenyong Yu, Yiheng Zhang, Yunkang Cao","submitted_at":"2024-06-11T15:02:16Z","abstract_excerpt":"Texture surface anomaly detection finds widespread applications in industrial settings. However, existing methods often necessitate gathering numerous samples for model training. Moreover, they predominantly operate within a close-set detection framework, limiting their ability to identify anomalies beyond the training dataset. To tackle these challenges, this paper introduces a novel zero-shot texture anomaly detection method named Global-Regularized Neighborhood Regression (GRNR). Unlike conventional approaches, GRNR can detect anomalies on arbitrary textured surfaces without any training da"},"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":"2406.07333","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-11T15:02:16Z","cross_cats_sorted":[],"title_canon_sha256":"37bc7771bd8bd05e112129c813afc90f823689d4e633d2b20af0d3c6b0a8a602","abstract_canon_sha256":"6111823a34587ac706b3a2be953f32a0be050fcfe94e9a737417c5411d2d3d86"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:30:21.774095Z","signature_b64":"IgJGXX8BeZ2YQOO2Z2WfFHOs/5SC9Q0FE8sthqUXFVSkVpGXdf50uyxLMIXottwJOc7+1aQ06PuPudD4BdOyDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"051646588279e04ebb1182b9aa9fb976ef8251f0ce4615ae0330534a268be8af","last_reissued_at":"2026-07-05T08:30:21.773490Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:30:21.773490Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haiming Yao, Wei Luo, Weiming Shen, Wenyong Yu, Yiheng Zhang, Yunkang Cao","submitted_at":"2024-06-11T15:02:16Z","abstract_excerpt":"Texture surface anomaly detection finds widespread applications in industrial settings. However, existing methods often necessitate gathering numerous samples for model training. Moreover, they predominantly operate within a close-set detection framework, limiting their ability to identify anomalies beyond the training dataset. To tackle these challenges, this paper introduces a novel zero-shot texture anomaly detection method named Global-Regularized Neighborhood Regression (GRNR). Unlike conventional approaches, GRNR can detect anomalies on arbitrary textured surfaces without any training da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.07333","kind":"arxiv","version":1},"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/2406.07333/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":"2406.07333","created_at":"2026-07-05T08:30:21.773552+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.07333v1","created_at":"2026-07-05T08:30:21.773552+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.07333","created_at":"2026-07-05T08:30:21.773552+00:00"},{"alias_kind":"pith_short_12","alias_value":"AULEMWECPHQE","created_at":"2026-07-05T08:30:21.773552+00:00"},{"alias_kind":"pith_short_16","alias_value":"AULEMWECPHQE5OYR","created_at":"2026-07-05T08:30:21.773552+00:00"},{"alias_kind":"pith_short_8","alias_value":"AULEMWEC","created_at":"2026-07-05T08:30:21.773552+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.13378","citing_title":"A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects","ref_index":187,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AULEMWECPHQE5OYRQK42VH5ZO3","json":"https://pith.science/pith/AULEMWECPHQE5OYRQK42VH5ZO3.json","graph_json":"https://pith.science/api/pith-number/AULEMWECPHQE5OYRQK42VH5ZO3/graph.json","events_json":"https://pith.science/api/pith-number/AULEMWECPHQE5OYRQK42VH5ZO3/events.json","paper":"https://pith.science/paper/AULEMWEC"},"agent_actions":{"view_html":"https://pith.science/pith/AULEMWECPHQE5OYRQK42VH5ZO3","download_json":"https://pith.science/pith/AULEMWECPHQE5OYRQK42VH5ZO3.json","view_paper":"https://pith.science/paper/AULEMWEC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.07333&json=true","fetch_graph":"https://pith.science/api/pith-number/AULEMWECPHQE5OYRQK42VH5ZO3/graph.json","fetch_events":"https://pith.science/api/pith-number/AULEMWECPHQE5OYRQK42VH5ZO3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AULEMWECPHQE5OYRQK42VH5ZO3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AULEMWECPHQE5OYRQK42VH5ZO3/action/storage_attestation","attest_author":"https://pith.science/pith/AULEMWECPHQE5OYRQK42VH5ZO3/action/author_attestation","sign_citation":"https://pith.science/pith/AULEMWECPHQE5OYRQK42VH5ZO3/action/citation_signature","submit_replication":"https://pith.science/pith/AULEMWECPHQE5OYRQK42VH5ZO3/action/replication_record"}},"created_at":"2026-07-05T08:30:21.773552+00:00","updated_at":"2026-07-05T08:30:21.773552+00:00"}