{"as_of":"2026-08-15T23:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e469674bba457c56caf4810f8341a2a92b6b69e1629c1cb45459273ea96c845f","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T21:58:08.483491Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2412.03969/citation-record","integrity":"/paper/2412.03969/integrity","json":"/paper/2412.03969/citation-record.json","paper":"/paper/2412.03969"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:58:09.223704Z","title":"Process manufacturing intelligence empowered by industrial JOURNAL OF LATEX CLASS FILES, VOL. 18, NO. 9, SEPTEMBER 2020 12 metaverse: A survey,","venue":null,"work_id":"f7c2bed5-423f-4fc6-b158-2c81ad0628fd","year":2020},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.255668Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:0a574cde4856e85da8f1ec1e11fec03d79b578e6407a537712f6496d3f255ce5","observation_id":"10222a4b-ae74-4505-aee4-6560d4766213","resolution":{"observed_at":"2026-08-11T21:58:09.228566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.209045Z","title":"Moninet with concurrent analytics of temporal and spatial information for fault detection in industrial processes,","venue":null,"work_id":"627b784c-4102-42d4-85c5-10cf5611dfde","year":2022},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.261304Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:8d2241cfc2267bd00b30242d00adc8d6b16d01cd7984ba877c6c02a31b442dd7","observation_id":"e7c97810-2eeb-4848-9762-7d805adbd74c","resolution":{"observed_at":"2026-08-11T21:58:09.213969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.193726Z","title":"Context-aware block net for small object detection,","venue":null,"work_id":"ea3824f2-407d-44a6-aec0-bc4502eee14c","year":2022},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.266109Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:c8e2ea43e562d5a59f827c74cb1faad8606b6722eb3d93f83f12260b59283635","observation_id":"6b0d8874-5cd8-4357-b9cf-0234cba8ec62","resolution":{"observed_at":"2026-08-11T21:58:09.198479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.179313Z","title":"Enhancing geometric factors in model learning and inference for object detection and instance segmentation,","venue":null,"work_id":"51724641-83ce-4a65-ac21-48f026be926c","year":2022},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.271004Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:56ae3ca6576fdd40fb0eac30bd05610f136b6c826b2196058498fa4a22a72b12","observation_id":"5c3a3630-77f0-4179-b885-add1955840e3","resolution":{"observed_at":"2026-08-11T21:58:09.184161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.164441Z","title":"Taanet: A task-aware attention network for weak surface defect detection,","venue":null,"work_id":"45fc14fe-15a7-4913-86d7-6d9475cbb7d1","year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.275995Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:489e826699c40aa1cc271e6a9d293d897ef870fc4bcd25ddfa27541f098291bf","observation_id":"a8f8e8da-a19c-4eae-a720-75dbce31dab9","resolution":{"observed_at":"2026-08-11T21:58:09.169283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.149397Z","title":"Clip-fsac: Boosting clip for few-shot anomaly classification with synthetic anomalies,","venue":null,"work_id":"9799577a-398f-40a9-a9ed-86055c07f8ef","year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.281000Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:6d2eb965307cc0ebc432ab14fa2d7654981dd9184a61abb41a0495cc3953c8eb","observation_id":"ea516408-b24b-4064-9e09-5328d820de83","resolution":{"observed_at":"2026-08-11T21:58:09.154526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.133973Z","title":"A reconstruction-based feature adaptation for anomaly detection with self-supervised multi-scale aggregation,","venue":null,"work_id":"605ff65f-fec6-42e6-b571-bd97ce7a7972","year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.286325Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:385cb24848f99d32d58c9e56f2be454253b5f208fec31b8335116dbe6146e91b","observation_id":"0c909ac2-1865-49f9-8d96-d58296188928","resolution":{"observed_at":"2026-08-11T21:58:09.139832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.290722Z","title":"Ultralytics YOLOv8,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.290722Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:53109c7f556d72c65ea2192180a16d7c32f01169c8036e0e8104f3e3329fc933","observation_id":"1a9cdd1a-cfbf-4bd5-b7b9-a840d7d16331","resolution":{"observed_at":"2026-08-11T21:58:08.290722Z","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":"2026-08-11T21:58:08.295113Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.295113Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:039bfc894f2e5654f4b57e6bace5be4b050eec686d4727501c946809d214fbe6","observation_id":"31db0ff7-10f1-4fda-939d-0fa00e84c367","resolution":{"observed_at":"2026-08-11T21:58:08.295113Z","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":"2026-08-11T21:58:09.101656Z","title":"Detrs beat yolos on real-time object detection,","venue":null,"work_id":"0deae17a-368a-4bb3-a7a0-9c6795270444","year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.299479Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:1d2f1d6bea45914f5ce8acefa8089bf686d90a051bde39345c3e511613c4108f","observation_id":"5251fbe8-0650-4dff-871d-c389d1dcef0d","resolution":{"observed_at":"2026-08-11T21:58:09.106156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.087421Z","title":"Yolo-hmc: An improved method for pcb surface defect detection,","venue":null,"work_id":"d7114f45-b319-4fd7-89c1-8ddc04feee97","year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.303902Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:f10a7a6d114f34274ba632dd014a21ae05667113594e979165e4b0bbfc7c73a9","observation_id":"b8a72868-814f-45cc-93a4-f5c70dfc3cc1","resolution":{"observed_at":"2026-08-11T21:58:09.092451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.073143Z","title":"Adin-detr: Adapting detection transformer for end-to-end real-time power line insulator defect detection,","venue":null,"work_id":"02f00497-5cd0-4335-a121-7758d776347d","year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.308435Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:080c1dccfb43deda587c17e1861646b45fa0f87759daa4685d6d12396b00802d","observation_id":"2b00e5a7-81cd-446e-b105-702340bd7a78","resolution":{"observed_at":"2026-08-11T21:58:09.077776Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.059200Z","title":"An optical lens defect detection method for micro vision based on wgso-yolo,","venue":null,"work_id":"e282568e-3c41-4d20-8fcc-7ed52dd0d298","year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.312795Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:895e30b4f08a920dc3b70d899db5fffbd371031f189456c58056c07b1a4a4731","observation_id":"b23ee4be-cb8a-45e5-9876-a345c077fc44","resolution":{"observed_at":"2026-08-11T21:58:09.064133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.045572Z","title":"Carafe: Content-aware reassembly of features,","venue":null,"work_id":"81df93f4-de56-4be1-a543-5ac8fcd7b377","year":2019},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.317292Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:285a5fa620f448fbb5a4a383729af8162077a8c25f2ad3e86834677ff445ada8","observation_id":"6bdeeef8-621e-4942-9be1-f577690306be","resolution":{"observed_at":"2026-08-11T21:58:09.050127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.031269Z","title":"An efficient anchor-free defect detector with dynamic receptive field and task alignment,","venue":null,"work_id":"0460daf2-b82a-48b4-8fa9-5bb67a93acd6","year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.321824Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:d02e5c8ea9a097b881b31740cbc907e96ffeb3a8e002286a9b660361382f3899","observation_id":"a76c5ad7-8a47-47bc-bdf5-75055fc4e6d9","resolution":{"observed_at":"2026-08-11T21:58:09.036215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.017461Z","title":"Mci-gla plug-in suitable for yolo series models for transmission line insulator defect detection,","venue":null,"work_id":"1b5dcfee-0d0d-46ee-bcfc-197255ec5431","year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.326290Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:c941f5207e7b3b48b1e9d921c7fe29725561ce22bd4f3a06ce1736c89f632067","observation_id":"f4d43317-c9f7-410e-9f2b-c4a134364e95","resolution":{"observed_at":"2026-08-11T21:58:09.022094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:09.002839Z","title":"Hripcb: a challenging dataset for pcb defects detection and classification,","venue":null,"work_id":"8a117659-8745-48ce-af7d-a27c01c25477","year":2020},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.330733Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:8a0627ba42d9ba77889c3ec5ec950fcedd245b72bba93166628f35771b6694fe","observation_id":"cffdafd7-4550-4f65-9011-23569840506e","resolution":{"observed_at":"2026-08-11T21:58:09.008049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.988703Z","title":"An end-to-end steel surface defect detection approach via fusing multiple hierarchical features,","venue":null,"work_id":"1fce1d08-a9f1-4a80-a2c7-24ca026b176d","year":2020},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.335239Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:b411529b097e0143b9c53b886f4058ac38bdd30de73107917155fc6b7611d226","observation_id":"366a8413-a541-4f91-b7ea-56d29db002a5","resolution":{"observed_at":"2026-08-11T21:58:08.993500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.974729Z","title":"Multilevel fine- grained features-based general framework for object detection,","venue":null,"work_id":"c18cb426-bb88-4778-8977-c178d73583d3","year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.339557Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:545fefe1fb2cbe9462d849fd3fb1b8e31753de5155f0db96a123d28d969140f7","observation_id":"69eb90f3-df36-4ab4-99d2-05fd7a4e5185","resolution":{"observed_at":"2026-08-11T21:58:08.979338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.959890Z","title":"Joining spatial deformable convolution and a dense feature pyramid for surface defect detection,","venue":null,"work_id":"f04a9490-2cc9-4070-8363-94c08e11b808","year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.343877Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:87e7d8be0f6c2ef8f64aac8a61ea4fef2016a1691a9d6eb5c67ebd6f30454394","observation_id":"8d25f3e6-5c2f-4efe-9291-27fe8b99db73","resolution":{"observed_at":"2026-08-11T21:58:08.964640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.945978Z","title":"Pcb-yolo: An improved detection algorithm of pcb surface defects based on yolov5,","venue":null,"work_id":"46ada31d-2c3e-43e6-8ea5-af00047ae43a","year":2023},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.348357Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:36a2434d1c73a1d7a92ce82ec7a2242b00791b6f7dadcca2c5a963a019bc11e3","observation_id":"1ee1eedb-f42c-4b5e-beec-89da35e5561f","resolution":{"observed_at":"2026-08-11T21:58:08.950413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.932449Z","title":"Canet: Contextual information and spatial attention based network for detecting small defects in manufacturing industry,","venue":null,"work_id":"6edc4389-508e-4641-9fae-833f2c22c338","year":2023},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.352887Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:4b8a5f0e4e5b6db2bb42245949715bf36c6c2cbbf3a2c677c3b66254e68f4486","observation_id":"fa5fd27f-2330-4e21-9483-7b9fafb8d0e2","resolution":{"observed_at":"2026-08-11T21:58:08.937298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.918553Z","title":"Hypergraph neural networks,","venue":null,"work_id":"a08413e8-ddbe-427c-8883-61bb0ad5d9f1","year":2019},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.357374Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:d67c58f44f80402234751d547aaf9ca8de9cb9244d5b8813b67eb1063ead5f85","observation_id":"55411ebb-f25a-4c9b-a7d7-2c0e93c47635","resolution":{"observed_at":"2026-08-11T21:58:08.923160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.904284Z","title":"HGNN+: General hypergraph neural networks,","venue":null,"work_id":"b2bc169a-a2bf-40c7-a1f2-e21947e60b0d","year":2023},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.361709Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:5401ef6d29890d26f4d718db3af701233b0bf0a48b92e561247712bce730d47b","observation_id":"a82559c6-b65e-4007-8727-265c8d87c384","resolution":{"observed_at":"2026-08-11T21:58:08.909113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.890134Z","title":"Lbsn2vec++: Het- erogeneous hypergraph embedding for location-based social networks,","venue":null,"work_id":"04916e7c-11f8-4f71-a1f6-82b65f2b8e67","year":2022},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.365899Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:7eae5617ec4e05812f9d6d51970589a3d74acc8999ae9eead0fd5b0cd80f4676","observation_id":"85cbef8e-cd58-4359-8972-6387355ef5d5","resolution":{"observed_at":"2026-08-11T21:58:08.895053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.876434Z","title":"Hypergraph factorization for multi-tissue gene expression imputation,","venue":null,"work_id":"5514e97e-f12b-4f52-acaa-36155bb63049","year":2023},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.370404Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:6a4ca7af526f6895d46f60b9b8156c2fe45e6f6a10834f7e7ccd7f68ac9ca0ee","observation_id":"cc6722a1-feb4-4f86-bec3-ef31647d439f","resolution":{"observed_at":"2026-08-11T21:58:08.881271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.862676Z","title":"Multi-hypergraph learning- based brain functional connectivity analysis in fmri data,","venue":null,"work_id":"d1a2c81a-a09a-4068-9a93-1a981b9e3adc","year":2020},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.374750Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:be65092249aeb700f886650321c901737ee2a031e3194a339d869c6fb12840c3","observation_id":"a11b531f-a8f0-4483-8ef7-44049403d103","resolution":{"observed_at":"2026-08-11T21:58:08.867386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04804","last_updated":"2024-10-16T07:20:58Z","snapshot_observed_at":"2026-08-12T23:06:16.129907Z","submitted_at":"2024-08-09T01:21:15Z","title":"Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.04804","snapshot_observed_at":"2026-08-11T21:58:08.380060Z","title":"Hyper-yolo: When visual object detection meets hypergraph computation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.380060Z"},"links":{"cited_paper":"/paper/2408.04804","citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:233d04948df026d8cebdd2262c1264131c925f8a9a388a0de8aca69333743f41","observation_id":"25b8ed64-2b5e-41b6-8574-3ba790f233d6","resolution":{"observed_at":"2026-08-11T21:58:08.380060Z","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":"2026-08-11T21:58:08.847714Z","title":"You only look once: Unified, real-time object detection,","venue":null,"work_id":"33d7891e-b19c-4556-ac0d-6389920561a9","year":2016},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.384909Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:26d7d3928fc14710f05978390eebd6b16cc019ed15c577f983a81c8299f7539b","observation_id":"c9e8bb8b-5d21-4fa9-8bd0-70210928de26","resolution":{"observed_at":"2026-08-11T21:58:08.852895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.834187Z","title":"Yolo9000: Better, faster, stronger,","venue":null,"work_id":"e0f41bea-f341-4f57-b84e-eb486f738551","year":2017},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.389168Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:4d33712127a98733ae925e03f09ace8cc5b164f54caaf5c41b93d657b8515c19","observation_id":"9de2864c-eacb-4121-82e4-d4def8d99796","resolution":{"observed_at":"2026-08-11T21:58:08.838590Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.08430","last_updated":"2021-08-06T03:22:14Z","snapshot_observed_at":"2026-07-06T11:30:06.143581Z","submitted_at":"2021-07-18T12:55:11Z","title":"YOLOX: Exceeding YOLO Series in 2021","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.08430","snapshot_observed_at":"2026-08-11T21:58:08.393436Z","title":"Yolox: Exceeding yolo series in 2021,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.393436Z"},"links":{"cited_paper":"/paper/2107.08430","citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:4441bd522aca29a7e0206622b504006c3c2b86cae1a11eb9824a394a235ba6f0","observation_id":"16f037a8-b133-4fb0-9eab-49fbc7d9c40b","resolution":{"observed_at":"2026-08-11T21:58:08.393436Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.02976","last_updated":"2022-09-07T07:47:58Z","snapshot_observed_at":"2026-08-13T14:31:50.103878Z","submitted_at":"2022-09-07T07:47:58Z","title":"YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.02976","snapshot_observed_at":"2026-08-11T21:58:08.398049Z","title":"Yolov6: A single-stage object detection framework for industrial applications,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.398049Z"},"links":{"cited_paper":"/paper/2209.02976","citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:91eb2cd3640cfb6e05f58fcd5138848420e95b9e78c1f37b8a40fbf54f91e37b","observation_id":"3e88a906-3e45-4ea5-82dd-89e11ceb337a","resolution":{"observed_at":"2026-08-11T21:58:08.398049Z","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":"2026-08-11T21:58:08.819804Z","title":"Repvgg: Making vgg-style convnets great again,","venue":null,"work_id":"58ddc5c4-d1af-4e57-b64e-897f8ca0cd51","year":2021},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.402969Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:ae3dc16e2b6b494780a0d140e4e7944544c879c4d01f99807d3b519188db2f26","observation_id":"05f2d923-6aad-4fb1-9eca-af4f6ff4772d","resolution":{"observed_at":"2026-08-11T21:58:08.825070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.805817Z","title":"Tood: Task- aligned one-stage object detection,","venue":null,"work_id":"0cfe1325-9612-478f-866d-87108a2387f9","year":2021},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.407218Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:099db957643bcb77aec31d9a54726a3524ef35e72e52227cd3656f660e7e4110","observation_id":"38cf8490-bceb-4218-b70e-0bf00d83d993","resolution":{"observed_at":"2026-08-11T21:58:08.810386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13616","last_updated":"2024-02-29T03:43:24Z","snapshot_observed_at":"2026-08-13T04:13:54.945776Z","submitted_at":"2024-02-21T08:42:53Z","title":"YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13616","snapshot_observed_at":"2026-08-11T21:58:08.411574Z","title":"Yolov9: Learning what you want to learn using programmable gradient information,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.411574Z"},"links":{"cited_paper":"/paper/2402.13616","citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:d70834f11a021f26e55a2d6b231ce3410f2bbc396f08548dc70fb82533b73fc7","observation_id":"35b70f23-ed36-475f-a15d-d2e8f36c8ba2","resolution":{"observed_at":"2026-08-11T21:58:08.411574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14458","last_updated":"2024-10-30T01:49:34Z","snapshot_observed_at":"2026-08-15T20:25:20.677192Z","submitted_at":"2024-05-23T11:44:29Z","title":"YOLOv10: Real-Time End-to-End Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14458","snapshot_observed_at":"2026-08-11T21:58:08.416576Z","title":"Yolov10: Real-time end-to-end object detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.416576Z"},"links":{"cited_paper":"/paper/2405.14458","citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:48a5a74c1d54f5dce9cc8bccf82d9d951e3c62de40f87affaea5c11e98b596b0","observation_id":"99fcf800-20f7-40e1-b8db-a6980dcd417b","resolution":{"observed_at":"2026-08-11T21:58:08.416576Z","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":"2026-08-11T21:58:08.790519Z","title":"Ultralytics YOLOv11,","venue":null,"work_id":"cf0337dc-37da-4ed3-a7ad-9949b88167e6","year":2024},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.421637Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:297953bca3f8cfe257d905ca018b6b10feefeab56c0d7b6e72c06546f0064134","observation_id":"306bf583-b17d-4a75-9b6a-de849ece3e94","resolution":{"observed_at":"2026-08-11T21:58:08.796377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.776034Z","title":"Real-time single image and video super- resolution using an efficient sub-pixel convolutional neural network,","venue":null,"work_id":"fd5d2a53-cf77-4e3e-8e7c-d81266ffad24","year":2016},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.426032Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:32959474b16b6bc41db76ad37762880458ed8561ebb8d2a00bb4b9a0f39896ee","observation_id":"eb7d51ff-5f87-4361-abf8-1008ebeaf48e","resolution":{"observed_at":"2026-08-11T21:58:08.780974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.02696","last_updated":"2022-07-06T14:01:58Z","snapshot_observed_at":"2026-08-13T15:09:35.261688Z","submitted_at":"2022-07-06T14:01:58Z","title":"YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.02696","snapshot_observed_at":"2026-08-11T21:58:08.430773Z","title":"Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.430773Z"},"links":{"cited_paper":"/paper/2207.02696","citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:5e058ff7deda79dce4142de43dccdd831ca52e98fe19d87cea1571839641d403","observation_id":"928dd12b-b885-481b-b7dd-2a4979990ed7","resolution":{"observed_at":"2026-08-11T21:58:08.430773Z","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":"2026-08-11T21:58:08.761459Z","title":"Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution,","venue":null,"work_id":"5df04ff4-834d-4b1f-9594-e3902d69382f","year":2021},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.436381Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:17937adb51393b4d949b48335f62dc9738b002b0ad7425a15a7d6fe1b47ce3d2","observation_id":"7ceaba4c-eea2-4bac-a346-273e0fd2f0f1","resolution":{"observed_at":"2026-08-11T21:58:08.766510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.747009Z","title":"Automatic detection and counting system for pavement cracks based on pcgan and yolo-mf,","venue":null,"work_id":"0d340958-ee79-440f-a61c-b3056f49014f","year":2022},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.442212Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:254a11da0d688a3b111ed09cb020564bb57713db42b6d9c5227aaca14d3f364e","observation_id":"c129f9f3-7e11-4021-acbb-d29b8a0f2198","resolution":{"observed_at":"2026-08-11T21:58:08.751766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.731709Z","title":"Es-net: Efficient scale- aware network for tiny defect detection,","venue":null,"work_id":"ffc26ab4-adee-4a13-a01b-a92feac7ae0d","year":2022},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.447060Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:94fd08c7af63fbe105d2b2153b7e96ea163cf304194039376d8cf004c7e91d3f","observation_id":"85a46d5b-456c-49b1-9a68-73aba9fe198a","resolution":{"observed_at":"2026-08-11T21:58:08.736657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.716444Z","title":"Deformable yolox: Detection and rust warning method of transmission line connection fittings based on image processing technology,","venue":null,"work_id":"b2d5ac88-31d5-4340-995c-51b26918aa96","year":2023},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.451571Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:fac370a3a04001445d4df7a95764f173f6e741e66b09f00a359a6438410c03d1","observation_id":"9212e5d4-404b-4024-a613-e79a49190e7f","resolution":{"observed_at":"2026-08-11T21:58:08.721998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.702066Z","title":"An anchor-free defect detector for complex background based on pixelwise adaptive multiscale feature fusion,","venue":null,"work_id":"ff7baaf6-f589-4ced-9b82-09378977b848","year":2023},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.456642Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:29fa403e3da8baebacf1339b04bb4d51e6f81be2adab36f1ae971e74ca11f90e","observation_id":"14bb40ec-85fc-46d3-b11d-d54915c498d9","resolution":{"observed_at":"2026-08-11T21:58:08.706684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.685061Z","title":"Attention network for rail surface defect detection via consistency of intersection-over-union(iou)- guided center-point estimation,","venue":null,"work_id":"b47d9bac-b652-45cd-adff-89f831d6f8db","year":2022},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.461189Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:f3b3ba8fdfd18f4f572c8d4dc3013f294eae9f2d5d0bd660bf2e4f58a36a8839","observation_id":"7d2f288f-7963-455f-9fd2-ee76964fff18","resolution":{"observed_at":"2026-08-11T21:58:08.691341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.668807Z","title":"Visual fault detection of multiscale key components in freight trains,","venue":null,"work_id":"d1652455-d117-4eda-951c-9b75e48a9b1d","year":2023},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.465727Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:eb0e028a1fdc20dd5fcd993cc2de734dfb06b356d7b5ca5195eeff5022f4398c","observation_id":"f2d61f8b-80c0-40f4-bff0-4200681d6eae","resolution":{"observed_at":"2026-08-11T21:58:08.674359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.654063Z","title":"Cascade r-cnn: High quality object detection and instance segmentation,","venue":null,"work_id":"fa7b2948-9c03-4c93-a9c5-d777f964fb2b","year":2021},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.470131Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:19efb19ec06c68e696aa0519a82c427e696b385ae451bcf772fbbc3efe3177a9","observation_id":"b700bf9c-50b1-41e1-b759-c57565cc3fca","resolution":{"observed_at":"2026-08-11T21:58:08.659063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.639003Z","title":"Libra r-cnn: Towards balanced learning for object detection,","venue":null,"work_id":"209d794a-bc31-4473-ad95-846f4a456ce5","year":2019},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.474566Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:4c341f36cc2c4928959604e8c8df3fb2e3286b4847f53639ca40f545bc9a325c","observation_id":"e2bc3acb-2122-455d-b06c-9f36afb8004c","resolution":{"observed_at":"2026-08-11T21:58:08.643914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.624068Z","title":"Focal loss for dense object detection,","venue":null,"work_id":"1d69c736-42e0-46eb-9c74-52cd2f915e2e","year":2017},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.479102Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:7e477e47ced3d979c8816f40654215e0d101bba76aeedfa52cb717f6181278f2","observation_id":"0bae6114-ba1d-423f-9051-607a434f3da6","resolution":{"observed_at":"2026-08-11T21:58:08.628859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T21:58:08.606148Z","title":"Fcos: Fully convolutional one- stage object detection,","venue":null,"work_id":"334e997b-fcc6-40f7-9594-6fb92b85b94f","year":2019},"citing_paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T21:58:08.483491Z"},"links":{"citing_paper":"/paper/2412.03969"},"observation_digest":"sha256:8cbb596d27140c6d342b88a6fdb62fcc154cd4993147a3a677ff7fd5f03ceab8","observation_id":"1afbb31b-929e-40fb-a454-2760f19b1d17","resolution":{"observed_at":"2026-08-11T21:58:08.612730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.03969","last_updated":"2024-12-05T08:38:01Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T00:51:48.355442Z","submitted_at":"2024-12-05T08:38:01Z","title":"HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":42},"total_outbound_references":50},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2412.03969."}