{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UHV66WR3FJZIWAOU7TMFXLMH42","short_pith_number":"pith:UHV66WR3","schema_version":"1.0","canonical_sha256":"a1ebef5a3b2a728b01d4fcd85bad87e6a1a5f5e0935b89a0e1f8444d1576a629","source":{"kind":"arxiv","id":"2506.18544","version":1},"attestation_state":"computed","paper":{"title":"Normality Prior Guided Multi-Semantic Fusion Network for Unsupervised Image Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guang Feng, Muhao Xu, Sijie Niu, Weiye Song, Xizhan Gao, Xueying Zhou","submitted_at":"2025-06-23T11:54:15Z","abstract_excerpt":"Recently, detecting logical anomalies is becoming a more challenging task compared to detecting structural ones. Existing encoder decoder based methods typically compress inputs into low-dimensional bottlenecks on the assumption that the compression process can effectively suppress the transmission of logical anomalies to the decoder. However, logical anomalies present a particular difficulty because, while their local features often resemble normal semantics, their global semantics deviate significantly from normal patterns. Thanks to the generalisation capabilities inherent in neural network"},"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":"2506.18544","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-23T11:54:15Z","cross_cats_sorted":[],"title_canon_sha256":"f06b51f1c51f38b67a950959d9ebef685be734bc0be7766197a9b50fcd65c0db","abstract_canon_sha256":"cb65fa8b76b1daecf0802efd9e82d93b526ee50e80e7db57810138c0f5e7e878"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:51.841267Z","signature_b64":"HplkyIii+jS9lK5GP7VoEXYoow9znZkLSF28G0EKSuu9c4FrFdF6viWlVEdlXV4saHcOgKtwEVeO/LFWzgBzAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1ebef5a3b2a728b01d4fcd85bad87e6a1a5f5e0935b89a0e1f8444d1576a629","last_reissued_at":"2026-07-05T11:25:51.840783Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:51.840783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Normality Prior Guided Multi-Semantic Fusion Network for Unsupervised Image Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guang Feng, Muhao Xu, Sijie Niu, Weiye Song, Xizhan Gao, Xueying Zhou","submitted_at":"2025-06-23T11:54:15Z","abstract_excerpt":"Recently, detecting logical anomalies is becoming a more challenging task compared to detecting structural ones. Existing encoder decoder based methods typically compress inputs into low-dimensional bottlenecks on the assumption that the compression process can effectively suppress the transmission of logical anomalies to the decoder. However, logical anomalies present a particular difficulty because, while their local features often resemble normal semantics, their global semantics deviate significantly from normal patterns. Thanks to the generalisation capabilities inherent in neural network"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.18544","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/2506.18544/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":"2506.18544","created_at":"2026-07-05T11:25:51.840840+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.18544v1","created_at":"2026-07-05T11:25:51.840840+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.18544","created_at":"2026-07-05T11:25:51.840840+00:00"},{"alias_kind":"pith_short_12","alias_value":"UHV66WR3FJZI","created_at":"2026-07-05T11:25:51.840840+00:00"},{"alias_kind":"pith_short_16","alias_value":"UHV66WR3FJZIWAOU","created_at":"2026-07-05T11:25:51.840840+00:00"},{"alias_kind":"pith_short_8","alias_value":"UHV66WR3","created_at":"2026-07-05T11:25:51.840840+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/UHV66WR3FJZIWAOU7TMFXLMH42","json":"https://pith.science/pith/UHV66WR3FJZIWAOU7TMFXLMH42.json","graph_json":"https://pith.science/api/pith-number/UHV66WR3FJZIWAOU7TMFXLMH42/graph.json","events_json":"https://pith.science/api/pith-number/UHV66WR3FJZIWAOU7TMFXLMH42/events.json","paper":"https://pith.science/paper/UHV66WR3"},"agent_actions":{"view_html":"https://pith.science/pith/UHV66WR3FJZIWAOU7TMFXLMH42","download_json":"https://pith.science/pith/UHV66WR3FJZIWAOU7TMFXLMH42.json","view_paper":"https://pith.science/paper/UHV66WR3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.18544&json=true","fetch_graph":"https://pith.science/api/pith-number/UHV66WR3FJZIWAOU7TMFXLMH42/graph.json","fetch_events":"https://pith.science/api/pith-number/UHV66WR3FJZIWAOU7TMFXLMH42/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UHV66WR3FJZIWAOU7TMFXLMH42/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UHV66WR3FJZIWAOU7TMFXLMH42/action/storage_attestation","attest_author":"https://pith.science/pith/UHV66WR3FJZIWAOU7TMFXLMH42/action/author_attestation","sign_citation":"https://pith.science/pith/UHV66WR3FJZIWAOU7TMFXLMH42/action/citation_signature","submit_replication":"https://pith.science/pith/UHV66WR3FJZIWAOU7TMFXLMH42/action/replication_record"}},"created_at":"2026-07-05T11:25:51.840840+00:00","updated_at":"2026-07-05T11:25:51.840840+00:00"}