{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JE6TS5IDLBR4EF66VQPAST2Z2D","short_pith_number":"pith:JE6TS5ID","schema_version":"1.0","canonical_sha256":"493d3975035863c217deac1e094f59d0eb8ac9acbd4cd1cf0ffa745299a1c857","source":{"kind":"arxiv","id":"2505.09926","version":2},"attestation_state":"computed","paper":{"title":"AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bin-Bin Gao, Chengjie Wang, Jiangtao Yan, Jun Liu, Lei Wang, Meng Wang, Weixi Zhang, Yong Liu, Yuezhi Cai, Yue Zhou","submitted_at":"2025-05-15T03:24:28Z","abstract_excerpt":"Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studies have demonstrated that pre-trained vision-language models like CLIP exhibit strong generalization with just zero or a few normal images. However, existing methods struggle with designing prompt templates, complex token interactions, or requiring additional fine-tuning, resulting in limited flexibility. In this work, we present a simple yet effective method called AdaptCLIP based on two key insights. First, adaptive"},"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":"2505.09926","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-15T03:24:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c0b258470697c894199e7c85b506b39ee06549b2bec0dc6da7141599d5ffb22d","abstract_canon_sha256":"dbbbb5288a33f9b2d05c6f7a311c6938182f756b745f6a581eea6a219c43aa11"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:51.695268Z","signature_b64":"16/my7XeCd67WBb7NgQaajacqhjoIIHCZDEvKbNNtZj6MuT4kUbZYUmK2ND/WHIg6eQ5E8PUTmt0FP78/w2aCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"493d3975035863c217deac1e094f59d0eb8ac9acbd4cd1cf0ffa745299a1c857","last_reissued_at":"2026-07-05T11:04:51.694752Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:51.694752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bin-Bin Gao, Chengjie Wang, Jiangtao Yan, Jun Liu, Lei Wang, Meng Wang, Weixi Zhang, Yong Liu, Yuezhi Cai, Yue Zhou","submitted_at":"2025-05-15T03:24:28Z","abstract_excerpt":"Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studies have demonstrated that pre-trained vision-language models like CLIP exhibit strong generalization with just zero or a few normal images. However, existing methods struggle with designing prompt templates, complex token interactions, or requiring additional fine-tuning, resulting in limited flexibility. In this work, we present a simple yet effective method called AdaptCLIP based on two key insights. First, adaptive"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.09926","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/2505.09926/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":"2505.09926","created_at":"2026-07-05T11:04:51.694816+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.09926v2","created_at":"2026-07-05T11:04:51.694816+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.09926","created_at":"2026-07-05T11:04:51.694816+00:00"},{"alias_kind":"pith_short_12","alias_value":"JE6TS5IDLBR4","created_at":"2026-07-05T11:04:51.694816+00:00"},{"alias_kind":"pith_short_16","alias_value":"JE6TS5IDLBR4EF66","created_at":"2026-07-05T11:04:51.694816+00:00"},{"alias_kind":"pith_short_8","alias_value":"JE6TS5ID","created_at":"2026-07-05T11:04:51.694816+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25273","citing_title":"CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28630","citing_title":"EntroAD: Structural Entropy-Guided Prompt Adaptation for Zero-Shot Anomaly Detection","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JE6TS5IDLBR4EF66VQPAST2Z2D","json":"https://pith.science/pith/JE6TS5IDLBR4EF66VQPAST2Z2D.json","graph_json":"https://pith.science/api/pith-number/JE6TS5IDLBR4EF66VQPAST2Z2D/graph.json","events_json":"https://pith.science/api/pith-number/JE6TS5IDLBR4EF66VQPAST2Z2D/events.json","paper":"https://pith.science/paper/JE6TS5ID"},"agent_actions":{"view_html":"https://pith.science/pith/JE6TS5IDLBR4EF66VQPAST2Z2D","download_json":"https://pith.science/pith/JE6TS5IDLBR4EF66VQPAST2Z2D.json","view_paper":"https://pith.science/paper/JE6TS5ID","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.09926&json=true","fetch_graph":"https://pith.science/api/pith-number/JE6TS5IDLBR4EF66VQPAST2Z2D/graph.json","fetch_events":"https://pith.science/api/pith-number/JE6TS5IDLBR4EF66VQPAST2Z2D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JE6TS5IDLBR4EF66VQPAST2Z2D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JE6TS5IDLBR4EF66VQPAST2Z2D/action/storage_attestation","attest_author":"https://pith.science/pith/JE6TS5IDLBR4EF66VQPAST2Z2D/action/author_attestation","sign_citation":"https://pith.science/pith/JE6TS5IDLBR4EF66VQPAST2Z2D/action/citation_signature","submit_replication":"https://pith.science/pith/JE6TS5IDLBR4EF66VQPAST2Z2D/action/replication_record"}},"created_at":"2026-07-05T11:04:51.694816+00:00","updated_at":"2026-07-05T11:04:51.694816+00:00"}