{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:PQAOKDJV2D2MONRTOFYIXH4MMY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"bc72345ee0bd0d2571b6a690d2ef18881e867ff9017ec75c1d39dc3645928e6d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-26T09:29:05Z","title_canon_sha256":"f399676ecb49d0a8b0b68a3e332c0150d78e0978fb7252c581d3c8f6be3b58ef"},"schema_version":"1.0","source":{"id":"2406.18197","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.18197","created_at":"2026-07-05T09:05:37Z"},{"alias_kind":"arxiv_version","alias_value":"2406.18197v4","created_at":"2026-07-05T09:05:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.18197","created_at":"2026-07-05T09:05:37Z"},{"alias_kind":"pith_short_12","alias_value":"PQAOKDJV2D2M","created_at":"2026-07-05T09:05:37Z"},{"alias_kind":"pith_short_16","alias_value":"PQAOKDJV2D2MONRT","created_at":"2026-07-05T09:05:37Z"},{"alias_kind":"pith_short_8","alias_value":"PQAOKDJV","created_at":"2026-07-05T09:05:37Z"}],"graph_snapshots":[{"event_id":"sha256:0b446770ab61c6683b285be79ab69ca895320ac9b5f9bbae9bf96a5a73b8103f","target":"graph","created_at":"2026-07-05T09:05:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2406.18197/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pre-trained vision-language models (VLMs) are highly adaptable to various downstream tasks through few-shot learning, making prompt-based anomaly detection a promising approach. Traditional methods depend on human-crafted prompts that require prior knowledge of specific anomaly types. Our goal is to develop a human-free prompt-based anomaly detection framework that optimally learns prompts through data-driven methods, eliminating the need for human intervention. The primary challenge in this approach is the lack of anomalous samples during the training phase. Additionally, the Vision Transform","authors_text":"Chao-Chun Chen, Feng-Hao Yeh, Jerry Chun-Wei Lin, Jia Ji, Pi-Wei Chen, Zih-Ching Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-26T09:29:05Z","title":"Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.18197","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:001e632bd2ba19f354de93e798e6174758f0deae64b07b77f11f0a889201e600","target":"record","created_at":"2026-07-05T09:05:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"bc72345ee0bd0d2571b6a690d2ef18881e867ff9017ec75c1d39dc3645928e6d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-26T09:29:05Z","title_canon_sha256":"f399676ecb49d0a8b0b68a3e332c0150d78e0978fb7252c581d3c8f6be3b58ef"},"schema_version":"1.0","source":{"id":"2406.18197","kind":"arxiv","version":4}},"canonical_sha256":"7c00e50d35d0f4c7363371708b9f8c66318bf09b11d80dd1e6531db771a4033c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7c00e50d35d0f4c7363371708b9f8c66318bf09b11d80dd1e6531db771a4033c","first_computed_at":"2026-07-05T09:05:37.003478Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:05:37.003478Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jeh5rDaWyLmBfFs6y5D3ppu0P7j85tW+m6EdgOnbkZrZar36VhCstSFeyMQSFeZdz4mRuVQ2aq8e+JFhNgBMBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:05:37.004042Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.18197","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:001e632bd2ba19f354de93e798e6174758f0deae64b07b77f11f0a889201e600","sha256:0b446770ab61c6683b285be79ab69ca895320ac9b5f9bbae9bf96a5a73b8103f"],"state_sha256":"200c97654805ce0ac9d7f5788706edf88b5d93c88bc19872192dfde37dc55c2e"}