{"as_of":"2026-08-22T19:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:817f966ff1135dcb67293aad087bf9246e6abb157492dd4b5aa594bc919145f7","coverage":[{"denominator":60,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":60,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:39:15.500609Z","state":"measured"},{"denominator":60,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":60,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.09264/citation-record","integrity":"/paper/2505.09264/integrity","json":"/paper/2505.09264/citation-record.json","paper":"/paper/2505.09264"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.166294Z","title":"In: NeurIPSW (2019)","venue":null,"work_id":"bef6a202-3396-4da8-afbd-5e3424b948a3","year":2019},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.275778Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:9bd3fd0012f81f75bf7ed6ff89e3a19a7a446dafe46c190b7b2a3f50a9079f78","observation_id":"314965ef-60b6-4e41-b726-9241f513551a","resolution":{"observed_at":"2026-08-15T21:39:16.169442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.155793Z","title":"In: NeurIPS (2013)","venue":null,"work_id":"f289aab7-2fc4-4d85-895c-de45e321ca11","year":2013},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.279730Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:5a3adf1943d5f81f4fca3b028dbf7dc242cde80c2f9ffe04ec174ec6a4c29cfc","observation_id":"ce1f6b70-d609-4529-b90e-571ee73dd519","resolution":{"observed_at":"2026-08-15T21:39:16.159288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.144885Z","title":"IJCV 130(4) (2022)","venue":null,"work_id":"04aaf23e-159d-4de2-ae5d-345a0955089c","year":2022},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.283049Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:bf5078921ed86ecbcaaa02c9218cfa07923e506704bb14a14a93d3b683e49c89","observation_id":"df0ecacf-ae5d-42d9-91d2-8da4303c5c0a","resolution":{"observed_at":"2026-08-15T21:39:16.148828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.133439Z","title":"In: CVPR (2019)","venue":null,"work_id":"303a448b-d962-462a-8b86-f4d2804025a4","year":2019},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.286366Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:b0e703f27bff57286b7772e83e9f86485b6b4a7bfd9ed1539eca53c731de1106","observation_id":"c5783e0a-79d2-4302-97ca-a1b5eab53655","resolution":{"observed_at":"2026-08-15T21:39:16.137298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.122426Z","title":"In: CVPR (2020)","venue":null,"work_id":"0f05150b-96cc-4e59-b92a-61099fa0ecb3","year":2020},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.290017Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:9b96a37bfaeb479f6aac9993d3ecea3f291b7ed6c5c7b9ef86dfa853d292288b","observation_id":"f4282897-2289-4b7b-adf1-a556b7f194e0","resolution":{"observed_at":"2026-08-15T21:39:16.125951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.110551Z","title":"In: VISIGRAPP (2019)","venue":null,"work_id":"48d06e52-8575-492c-9250-83e97370067f","year":2019},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.293452Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:b8b3f4155cd33c2300a14537b184f452da2fe258dc094049b7ae2ad27b5138a5","observation_id":"040944b6-0582-4938-9a69-2ac418c8ef22","resolution":{"observed_at":"2026-08-15T21:39:16.114291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10724","last_updated":"2023-05-18T05:52:06Z","snapshot_observed_at":"2026-08-20T15:40:46.425055Z","submitted_at":"2023-05-18T05:52:06Z","title":"Segment Any Anomaly without Training via Hybrid Prompt Regularization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10724","snapshot_observed_at":"2026-08-15T21:39:15.297779Z","title":"arXiv:2305.10724 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.297779Z"},"links":{"cited_paper":"/paper/2305.10724","citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:92528ad3a5b3977442ff8c62b355959dd1531fad3a575fbc3dfa613c668b6e92","observation_id":"9adf6d23-b49e-4c68-96f6-a4dc4c2d517d","resolution":{"observed_at":"2026-08-15T21:39:15.297779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.099440Z","title":"In: ICCV (2023)","venue":null,"work_id":"b94c9bb9-2b21-4120-897c-2e68bd13b088","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.301765Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:0395f0b03bb54855a7c39cd9874246d742893518bc4f110d55fb6ffecff24082","observation_id":"94eb86d0-147c-4c75-bdd5-66aa153811ff","resolution":{"observed_at":"2026-08-15T21:39:16.103167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.306303Z","title":"In: ICML (2006)","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.306303Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:55fee76435433034e38525aec482e3675736058e3c1bd2365c8f832ea130649b","observation_id":"5a3a506d-7b29-4951-84f3-c83fbbb48a71","resolution":{"observed_at":"2026-08-15T21:39:15.306303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.080141Z","title":"In: ICPR (2021)","venue":null,"work_id":"12914508-d966-44e0-9ae8-9075ea2ed41d","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.309903Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:ec78e810b8197dd9a8772bbcbeba7957ae32e131e5b9989f9fbbcd5db3aad46d","observation_id":"3f7a0653-31ac-48d5-81d1-5406cb574d24","resolution":{"observed_at":"2026-08-15T21:39:16.084085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.070997Z","title":"In: CVPR (2022)","venue":null,"work_id":"af9f0147-5d9d-4e75-aa2d-3b4fadc4b780","year":2022},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.313611Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:e9134ea8453cf8fcc40a2f50e1f11be594da2b4fbfc3a46a68a8f09baae1b2da","observation_id":"73d97766-593f-40e2-9cfb-bb8e7943cf26","resolution":{"observed_at":"2026-08-15T21:39:16.073921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.061441Z","title":"In: CVPR (2022)","venue":null,"work_id":"c69bd6a2-4f4f-41f3-802a-5a50cb295333","year":2022},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.317293Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:2681b4810977270cdbe20d20dc6fa010f7ae2aa953a23119f2fa7cc3ea9b187c","observation_id":"827fdb1c-d633-4b49-8681-2a2f160070ba","resolution":{"observed_at":"2026-08-15T21:39:16.064752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.050861Z","title":"In: CVPR (2021)","venue":null,"work_id":"1ca8dd2a-6f3b-472e-b5ee-832ac5f9b94c","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.322035Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:b247dbffad35d904649f31ed0e52d5246e9c6b81b442053c2a2edcf4f5e26c99","observation_id":"86ca4745-7d1c-4d3b-934d-a4bc0fcf9259","resolution":{"observed_at":"2026-08-15T21:39:16.054547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.039656Z","title":"In: ICCV (2019)","venue":null,"work_id":"d4845ab6-35f1-45df-9ea5-d7ca8065e16c","year":2019},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.326034Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:192c06204a73da985cbbb5a2616c328ff0ff0335c4d584970d64771b43a56030","observation_id":"3765975a-b1b2-42a9-afe8-d124cda75475","resolution":{"observed_at":"2026-08-15T21:39:16.043438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.028131Z","title":"In: AAAI (2024)","venue":null,"work_id":"fa04e4d3-5244-418e-8476-129f573b5a3a","year":2024},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.329606Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:2846635f018c7f0229233c1a71d5f8055edb356b803cfaff291b85650b1163ba","observation_id":"706ac6b0-c724-48aa-8f84-b4a1002600de","resolution":{"observed_at":"2026-08-15T21:39:16.032059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.016936Z","title":"In: ICCV (2021)","venue":null,"work_id":"8db61bb9-17b6-44cb-adb5-ebf87b768752","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.333305Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:c0c8868dc61425f70a66320bf39b1ac2fe87da984a041febfef24fc2461129c8","observation_id":"7c83272d-9b69-413c-b69e-d3d164ba1be9","resolution":{"observed_at":"2026-08-15T21:39:16.020778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:16.005821Z","title":"In: CVPR (2024)","venue":null,"work_id":"a4706a27-1c88-4a35-bc4e-33a416d8cf5f","year":2024},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.336976Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:4d3abeb99bdd1b795c27a6d6447b2f118665360c56f828731c4ea175894b25ba","observation_id":"c8acdb8f-9a06-4894-ba2b-19a0d711efc3","resolution":{"observed_at":"2026-08-15T21:39:16.009627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.993399Z","title":"In: CVPR (2023)","venue":null,"work_id":"634b693e-ca0f-41a3-9f6d-dd985569db7b","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.340646Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:55240bfbedf5805da6044244a2825434c8d98352f17a3035f3e771468e1666ca","observation_id":"cd95bc8a-ddcb-4cfb-bfd0-46dcefb2a4da","resolution":{"observed_at":"2026-08-15T21:39:15.997082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.982516Z","title":"npj Digit","venue":null,"work_id":"2e651330-265d-48ca-a456-21ea0531aece","year":2022},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.344583Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:a46b42174af02498b565898ec87ed717299c76b72c1cdf605a3b44ed640af409","observation_id":"981b7b88-362e-4a2b-9284-cf1b3511f990","resolution":{"observed_at":"2026-08-15T21:39:15.985743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.973222Z","title":"IEEE Access10 (2022)","venue":null,"work_id":"8dc603d0-7cbe-44da-891e-62a85680a00d","year":2022},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.348307Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:019da39496787029eed953ed96655e7ed2cc8ef67fd39d207d9d7c00cd7d2664","observation_id":"6f9d1815-22db-4f6b-8c09-93712c17d51d","resolution":{"observed_at":"2026-08-15T21:39:15.976439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.962644Z","title":"IEEE Access10 (2022)","venue":null,"work_id":"5c05820e-4233-41f7-893b-ee510e3dc2b2","year":2022},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.352268Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:c1f2227eee0a451661776124af0cd825b943a44b512b10df4ea05386038e22a9","observation_id":"6225329e-b125-4f4d-a3fa-e41ab82e0fa3","resolution":{"observed_at":"2026-08-15T21:39:15.966273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.951601Z","title":"In: CVPR (2023)","venue":null,"work_id":"670aefdd-09ab-4629-bd83-63606ad676bd","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.355680Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:5d07b6fb54703393351087013704025835b8997dcdcbdbeeb01d090d7bdf956f","observation_id":"4ddc2d46-922f-482d-86b0-5e6484716678","resolution":{"observed_at":"2026-08-15T21:39:15.955248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.939675Z","title":"In: CVPR (2021)","venue":null,"work_id":"74bcc723-b654-4931-8632-92e900ec2df2","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.359251Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:d726bd972390e65cd7055fefca938d22305f21651c260c8cc9ddda88143c0d49","observation_id":"38ae4bbe-2f66-4acc-8d88-c89fa7172e38","resolution":{"observed_at":"2026-08-15T21:39:15.943778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.927560Z","title":"In: CVPR (2023)","venue":null,"work_id":"653913e6-3bcc-4664-9374-619694d55f20","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.362410Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:df675356edaf02af84e9272a64fcc3f11e6dea7da7f54573fbacad0152cdcb94","observation_id":"7c7cb043-cffb-41ad-a2e2-121fb1a19425","resolution":{"observed_at":"2026-08-15T21:39:15.931982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.915091Z","title":"In: CVPR (2023)","venue":null,"work_id":"6d7cd911-4b4f-49a9-9168-05010069e9e9","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.366049Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:ca391dc736e90e55eca0f3a95427947c7382c52d6aa79b86f97f398facced1bd","observation_id":"248cade2-9317-40f3-953c-1f107dc8b1f9","resolution":{"observed_at":"2026-08-15T21:39:15.918873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.903193Z","title":"In: SIE (2021)","venue":null,"work_id":"a1dfc407-f8ba-4edf-b296-fc728c92a3c7","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.369418Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:79986d793018dcd5738a8d3bed3edac53ce32d758d50d0693d197e50dcb41390","observation_id":"ff20e0de-c393-4aeb-836a-8e4d8c2a9864","resolution":{"observed_at":"2026-08-15T21:39:15.906938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.893389Z","title":"In: ICLR (2023)","venue":null,"work_id":"ec18709f-ae69-440d-8a75-3e60c34812f4","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.374136Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:0666f6e354db007de8b231e82eee92fad288a1d210058e2c857367ef57695267","observation_id":"8ac1d58e-fa83-40ae-accb-737febdec912","resolution":{"observed_at":"2026-08-15T21:39:15.896681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.882936Z","title":"In: CVPR (2020)","venue":null,"work_id":"472efa75-c3f3-415d-a6bb-eb17641916e1","year":2020},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.377936Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:252b511ddc9e0f397142faf3f6971d45d6357dc0d6e5873011c9ffb722455685","observation_id":"142d7a36-36cd-425b-80dd-231cc51233e8","resolution":{"observed_at":"2026-08-15T21:39:15.886815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.870925Z","title":"In: CVPR (2019)","venue":null,"work_id":"ea0ecbd0-ef8f-4fda-aad4-221ee5e2a8fb","year":2019},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.381723Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:59da3d8d9e89b60079235d252c4221e7bfd20d73a9b13f43bd0ca137362c5ce9","observation_id":"0928f941-6f44-4226-a052-15c401daeda9","resolution":{"observed_at":"2026-08-15T21:39:15.875525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.859410Z","title":"ACMSCG19(3) (2005)","venue":null,"work_id":"54dbecf8-a67f-4a9c-a590-48da2ea154ef","year":2005},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.386774Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:b2e5aa9edc51808408f542d60664ce9c0d750f850863472409381b6d23e50be2","observation_id":"d7124167-24f1-4e28-ac54-025469253055","resolution":{"observed_at":"2026-08-15T21:39:15.863250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.391187Z","title":"In: ICML (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.391187Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:5743f3e29714a01f8ae7e5bd58f0c9efbfc8905de55fb123cffaca768c064799","observation_id":"63924b0d-42ad-4506-83bb-7224dcb61a4d","resolution":{"observed_at":"2026-08-15T21:39:15.391187Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.840402Z","title":"Nature Neuroscience 2(1) (1999)","venue":null,"work_id":"4e3f2441-6881-49b5-9be0-c33001d7a205","year":1999},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.395073Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:29eca476a7134be3943131dea93ff46d382cecb85baffe0ff916e31a78bc5fbe","observation_id":"435c4bee-667f-4d84-b04d-61ffd622b7c4","resolution":{"observed_at":"2026-08-15T21:39:15.844220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.828903Z","title":"In: CVPR (2021)","venue":null,"work_id":"a1b870bb-e165-4940-9113-b9d25d9fe160","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.399968Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:efe4d47943d9480ad0e8e4d2b7166b5c06672f0c052cc462c896216fc1a21c52","observation_id":"47eca881-ce54-4ecd-a6b2-c8da8d9fcf7f","resolution":{"observed_at":"2026-08-15T21:39:15.832905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.817699Z","title":"In: ICPR (2021)","venue":null,"work_id":"c2694f41-0f26-4074-ab61-161f2c898414","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.404624Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:ac3302336818ddf71847bb347d749e129c1bc2d426cbe346b2ba62c93f5c9a1d","observation_id":"46c26940-457b-4cd5-9f6a-9744e2cf98e3","resolution":{"observed_at":"2026-08-15T21:39:15.821629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.807109Z","title":"In: CVPR (2022)","venue":null,"work_id":"8b00419a-735d-4006-b237-d7042b7b208f","year":2022},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.408352Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:d446666f3cb681d5f572522e68c8958f68d9a3d0dd0a6ecee32267d46966c6f9","observation_id":"37751169-3f6f-4d4b-8a61-a57a5623412e","resolution":{"observed_at":"2026-08-15T21:39:15.810666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.412323Z","title":"In: CVPR (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.412323Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:d3c0feb491a54bb3f49e2cc70fc4a7f167093ff1c06d772e17a603e85be05821","observation_id":"bc3fd30b-63c4-4313-ad45-e2de39bf95a0","resolution":{"observed_at":"2026-08-15T21:39:15.412323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.789851Z","title":"In: CVPR (2022)","venue":null,"work_id":"0bc937ba-a193-453a-ba81-8ae1428bb16c","year":2022},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.416242Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:07f0670436ea1cbf0b941aa510f8151b10b95ce41b4f2ed001d20f59a2d4d47e","observation_id":"31a1d136-d0e0-409e-a70a-df2bd3c636c1","resolution":{"observed_at":"2026-08-15T21:39:15.793099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.778134Z","title":"In: WACV (2022)","venue":null,"work_id":"5e876273-f1be-4815-9a17-e0da2145f130","year":2022},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.419586Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:c93724cbbddeb27c5de50ab58fa4d2bdd59b0710b40c88eaa113ffe6704e710a","observation_id":"cf939880-11ec-4355-b3ec-3ff21392b8f1","resolution":{"observed_at":"2026-08-15T21:39:15.782582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.765329Z","title":"In: CVPR (2021)","venue":null,"work_id":"7ec6ccf8-e736-41ed-a9d5-a711aae21b00","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.422597Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:f0f9de5eecd243d7317d5e6d60c7ec26b2f0d1be638baa65628f87df9f18ad97","observation_id":"7f7a78c5-fad2-452a-922e-5a134bc5e47a","resolution":{"observed_at":"2026-08-15T21:39:15.769118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.425609Z","title":"In: CVPR (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.425609Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:47a30388626d6f9913cc6c56fde2750857e7a02b1daec64802ad975d0c5916cf","observation_id":"d6797d6a-0023-4515-8d78-088f4d01daf4","resolution":{"observed_at":"2026-08-15T21:39:15.425609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.428718Z","title":"In: ICML (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.428718Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:b14e6f2d103e7309ca98ccc7955694224f5b62e55f8326832fba0d559edd7fab","observation_id":"37a10cea-38fe-432a-89a5-2cddf8948535","resolution":{"observed_at":"2026-08-15T21:39:15.428718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.739556Z","title":"In: CVPR (2023) OneNIP 17","venue":null,"work_id":"8bb4ae7e-f8de-4539-9400-07c778d20879","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.431760Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:b3c11f074d565fa2cea2cfb9d5e36b98f7ad7cbeab4606fd2c1166401462f935","observation_id":"6bc321ba-679a-40b1-b3af-c20f21b7df72","resolution":{"observed_at":"2026-08-15T21:39:15.743542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.435287Z","title":"In: NeurIPS (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.435287Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:fbbd98936c4b166bbf37e76fbb73fbfb7d7cc70ae70619ce5e38ddbe045189fc","observation_id":"b89c9350-7ee0-4de6-818b-79638645dffd","resolution":{"observed_at":"2026-08-15T21:39:15.435287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.721978Z","title":"In: ICML (2008)","venue":null,"work_id":"acd197c7-f84c-4c87-bd2f-f864e585709f","year":2008},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.439938Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:b558dbb7fc49aabc0414ed0e6d23d2ae6c957ea26d94a269f3d265f2e85ee65a","observation_id":"2ca018c4-120c-49cb-9f1b-ddf20affa9d6","resolution":{"observed_at":"2026-08-15T21:39:15.725515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.711885Z","title":"BMVC (2021)","venue":null,"work_id":"e1f86f77-d94f-4c89-898e-9d6092e24813","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.444917Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:542a51a60b349189efc97985693d6d1a30c301fb6e2583758271e60defc201c9","observation_id":"d69f1e16-087b-4a30-b77a-56121ff95e93","resolution":{"observed_at":"2026-08-15T21:39:15.715176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.702658Z","title":"In: CVPR (2021)","venue":null,"work_id":"af722af1-74d9-4fe7-9ced-79a998eeaf56","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.448483Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:f98b0da8e4946155ad14bbbd93863b3bb6fe0974aada5df5eec6a434dea3c850","observation_id":"148b72e8-8d50-4bd0-b02e-ee4a5741c4ef","resolution":{"observed_at":"2026-08-15T21:39:15.705597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.692342Z","title":"In: ICPR (2021)","venue":null,"work_id":"6c3f86ef-7b0a-49b1-bca1-8cc16ed2feb5","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.452382Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:6178ab4f2c87dc7dfb206844bfcc008744355170c5732ccb02da9c347c4a1b3f","observation_id":"c39a2af5-190e-4532-a30c-61b0bd0702df","resolution":{"observed_at":"2026-08-15T21:39:15.695658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.682094Z","title":"In: CVPR (2023)","venue":null,"work_id":"00a741b3-d276-4fb8-aba0-fe26a3836608","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.456289Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:e6ef888d1eeb16b644c07a76d38d918b4556b7dda76457b5a75e7ec19048b419","observation_id":"127ce6fa-488c-4cff-b687-71aae4a4fddc","resolution":{"observed_at":"2026-08-15T21:39:15.685529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.671502Z","title":"In: AAAI (2021)","venue":null,"work_id":"af6cbb44-ebec-4f95-ab5c-f72f822bc533","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.460124Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:8a1eea1241b9d9a79ae6ba018421bfac288fac94a9173957f6505aff2c4aff6a","observation_id":"91bc7321-0ece-4b72-a875-9480f7ca2255","resolution":{"observed_at":"2026-08-15T21:39:15.675158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.660788Z","title":"In: ICCV (2023)","venue":null,"work_id":"0d3259fc-4527-42f5-b56d-842c98307408","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.463849Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:65b2b40ce116fc3ea4f76de7fe9c2bc37764e81bc63c43245cb150a7f1275110","observation_id":"63464bf3-cc40-4797-ae0b-7a927036248b","resolution":{"observed_at":"2026-08-15T21:39:15.664403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.649047Z","title":"In: CVPR","venue":null,"work_id":"26d9c083-007e-4d3a-8784-0b72605bd9f4","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.467648Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:034f1b0cd0902c1194136bed32bce12e130010c458931c7c0df7c4a0381dde75","observation_id":"78182aa1-c072-44a7-9263-8f05e550061c","resolution":{"observed_at":"2026-08-15T21:39:15.652856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.638100Z","title":"In: NeurIPS (2022)","venue":null,"work_id":"5e3b21c5-3d0e-4572-b5a5-297ba018b138","year":2022},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.471106Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:e3274fc83999299d1fafb5423026e42613e231dc4f8822781f2741bdc08048e1","observation_id":"d9f10d65-6119-44e9-8faf-7e70386ba00d","resolution":{"observed_at":"2026-08-15T21:39:15.641881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.627348Z","title":"In: CVPR (2020)","venue":null,"work_id":"e77d3ac0-4e9f-40aa-b62c-851a38310afd","year":2020},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.475079Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:d112078160fcb4f4adafd1f1d285029a9769c69a6ba543ceb7fe1efab4e6c584","observation_id":"820352be-49a0-4e0e-9ec2-6ae57ac67522","resolution":{"observed_at":"2026-08-15T21:39:15.630903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.617052Z","title":"In: ICCV (2021)","venue":null,"work_id":"552082f8-a47a-46e7-ace2-3bc18543a02f","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.478841Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:db8e6875f74e4f6303ac6ea1c75ee805dfeeb84b3f5e312aca34c62924974482","observation_id":"3e797aaa-7b6a-4829-bf25-82e3ccd46496","resolution":{"observed_at":"2026-08-15T21:39:15.620503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.604995Z","title":"PR112 (2021)","venue":null,"work_id":"0af10442-afdd-4960-9c8a-d1fa7ff3706f","year":2021},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.482295Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:41653304057baa083ed27d3de7adb56c3df77b863bb3c9d93f649e8fcb5054e1","observation_id":"3e1e6a97-fb2f-4f86-8e6e-1ddd887f8596","resolution":{"observed_at":"2026-08-15T21:39:15.608951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.592215Z","title":"In: CVPR (2023)","venue":null,"work_id":"3e77258d-74c4-4e01-8603-9377343cfe10","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.486507Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:df1db9c86923e07fbc8e0855bff16dc37b3e30a635ee5c299a564ad4981c704f","observation_id":"73448c64-77cf-4f11-875e-e433b9fa9edc","resolution":{"observed_at":"2026-08-15T21:39:15.597094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.579751Z","title":"In: CVPR (2023)","venue":null,"work_id":"ff33ae14-5643-4a17-a0b6-bdf953164960","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.490021Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:35c7cc51e81008e8b0e8555e2a88e14428bee075720f2267fe99898bbcea576a","observation_id":"c42635bd-8db6-4c15-8472-4f743b683553","resolution":{"observed_at":"2026-08-15T21:39:15.584297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.567809Z","title":"In: CVPR (2023)","venue":null,"work_id":"6e6a56ef-a44c-4249-9561-5c2e04319546","year":2023},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.493455Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:e778d6e0afe0f9ebcd4d8abd4322b3cc97a6ee0402df0a65aeb170b13aaec219","observation_id":"2d0cb876-7091-4880-b461-0ccd6ff8396e","resolution":{"observed_at":"2026-08-15T21:39:15.571696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.554589Z","title":"In: ICLR (2024)","venue":null,"work_id":"c8f52c7b-6421-40f9-afc5-54cb48d21fff","year":2024},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.497034Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:1213cdaccc3750da6c567617313a6d1ddf6c285cc69e5d85c2025be022f5175a","observation_id":"edb455f3-fe65-4257-8478-15eb0eaedb6f","resolution":{"observed_at":"2026-08-15T21:39:15.560074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:39:15.541116Z","title":"In: ECCV (2022) OneNIP 1 A Implementation Details For fair comparisons, we maintain the same hyper-parameters as in UniAD [52]","venue":null,"work_id":"bc055493-8a39-43a2-9751-a918b0f265e3","year":2022},"citing_paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T21:39:15.500609Z"},"links":{"citing_paper":"/paper/2505.09264"},"observation_digest":"sha256:d6334e4a48771e31cae7aec234719509c548f05ff806b0cc941dad149c7c7af5","observation_id":"7b8bac25-e814-4805-82d0-9dff7f520bd8","resolution":{"observed_at":"2026-08-15T21:39:15.545792Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.09264","last_updated":"2025-05-14T10:25:14Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-18T09:06:27.404183Z","submitted_at":"2025-05-14T10:25:14Z","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt"},"reference_resolution":{"displayed":60,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":52},"total_outbound_references":60},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2505.09264."}