{"as_of":"2026-08-07T08:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1abf474dbef5ee5785526611b9aa09e19cc7ce16fda7e162e78d81cd43a68ffd","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T05:15:43.142692Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-23T01:43:12.464857Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-23T01:45:18.363741Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"cited_work":{"arxiv_id":"2508.02067","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.02067","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"& Bhatia, R","venue":null,"work_id":"68ec1d84-d51c-407b-b99f-caab8cec182f","year":2025},"citing_paper":{"arxiv_id":"2503.01605","last_updated":"2026-02-12T17:20:10Z","snapshot_observed_at":"2026-07-06T20:45:46.299882Z","submitted_at":"2025-03-03T14:41:06Z","title":"A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-23T01:43:12.464857Z"},"links":{"cited_paper":"/paper/2508.02067","citing_paper":"/paper/2503.01605"},"observation_digest":"sha256:08cddb6f0a3945cc32f74a2f4b2558f855d63ab6e511a31d496c7305fbea239a","observation_id":"c0c15770-124b-4ae6-a7ba-4806f029e80a","resolution":{"observed_at":"2026-05-23T01:45:18.365716Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2508.02067/citation-record","integrity":"/paper/2508.02067/integrity","json":"/paper/2508.02067/citation-record.json","paper":"/paper/2508.02067"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T05:15:44.176632Z","title":"Rich feature hierarchies for accurate object detection and semantic segmentation,","venue":null,"work_id":"aa616f5f-cbd7-493a-85a9-4a6a105595a6","year":2014},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:41.847996Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:9ba94a3424a97c49e613bfb93b75ae44affe1f88754a9ef8fac6462f26174a89","observation_id":"4b25333d-e22f-494f-ada5-55e91c063b7b","resolution":{"observed_at":"2026-08-06T05:15:44.186712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:44.149370Z","title":"Fast r-cnn,","venue":null,"work_id":"afd8e3d2-585a-47d3-b241-52f3bc2cd1e1","year":2015},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:41.888996Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:ff6fe26c0067936d33971e9de1c6607d366a5b4930f69a640772dea9d7dbddd4","observation_id":"bfa631c0-09d8-4367-b6f6-5c211c54900b","resolution":{"observed_at":"2026-08-06T05:15:44.160730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:41.926887Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:41.926887Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:ddeb7a615b169bd6defe91b19022581c41a74fe274ca518ddc58e92258b81517","observation_id":"8c8fd7b9-7e36-4393-9d47-9f6275fed9cf","resolution":{"observed_at":"2026-08-06T05:15:41.926887Z","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":"2026-08-06T05:15:44.106806Z","title":"You only look once: Unified, real-time object detection,","venue":null,"work_id":"c1ced728-6728-418e-b971-f8ca4a8cb691","year":2016},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:41.953260Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:dfbea1f6c231a308aeb458468f03136f9c6592c8c2f5e5658f2350ccb5a7609a","observation_id":"3365a331-abfc-4b52-9964-792ad93839cd","resolution":{"observed_at":"2026-08-06T05:15:44.118481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:44.077721Z","title":"Discriminatively trained deformable part models, release 1,","venue":null,"work_id":"a5a08d59-a9ec-4a38-b44a-6b3274365d6c","year":2008},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:41.975818Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:26b7846e8a649bc3003534f76de4ebb14415c50366353b000fb82eb069509bc4","observation_id":"b35f7424-82de-4685-8cd8-5bf20cf0109d","resolution":{"observed_at":"2026-08-06T05:15:44.091928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:44.052961Z","title":"Se- lective search for object recognition,","venue":null,"work_id":"b9a5629e-8732-4c60-8d29-a16fe823bebc","year":2013},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.006280Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:94be09579712affd0582a82ae6372f8d8470477aece8aceb21ebdf5d336f14d8","observation_id":"c65500cf-e25b-4243-ad15-8a327b212def","resolution":{"observed_at":"2026-08-06T05:15:44.063758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:42.041377Z","title":"Ssd: Single shot multibox detector,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.041377Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:3ccba9d074977e19e5f099d83247864f04efa4e3a7a22677082497ebfc8d306e","observation_id":"152dc88d-bd2f-497d-9426-ec25b0b25f87","resolution":{"observed_at":"2026-08-06T05:15:42.041377Z","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":"2026-08-06T05:15:42.071044Z","title":"Going deeper with convolutions,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.071044Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:99787060b43346a7c461e8f79a87f159f369248703fe46305bdeb0df1b217cf4","observation_id":"156d666a-c5e6-4273-9bb9-1a5895090761","resolution":{"observed_at":"2026-08-06T05:15:42.071044Z","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":"2026-08-06T05:15:44.012221Z","title":"Yolo9000: Better, faster, stronger,","venue":null,"work_id":"9a34ffa6-9857-4a05-9f17-b482f489e3b1","year":2017},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.091380Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:37eea472a1b543fd4b3c4c7b0d9b0857343780c54e179a2a5cb99b299f591db7","observation_id":"95576dc5-6cba-4b90-808d-4f46ff599808","resolution":{"observed_at":"2026-08-06T05:15:44.023103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:43.983854Z","title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift,","venue":null,"work_id":"28501332-bb5b-4394-b743-9c80f9b56b08","year":2015},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.126454Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:b987ab172c8343ce1653e68f163f2b871eaf3aee027c89b2c1c08f1991d8c4b5","observation_id":"ad6483d6-4874-4550-9a8a-d7fc7058bac0","resolution":{"observed_at":"2026-08-06T05:15:43.996159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.02767","last_updated":"2018-04-08T22:27:57Z","snapshot_observed_at":"2026-08-06T11:09:16.409556Z","submitted_at":"2018-04-08T22:27:57Z","title":"YOLOv3: An Incremental Improvement","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.02767","snapshot_observed_at":"2026-08-06T05:15:42.161362Z","title":"Yolov3: An incremental improvement,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.161362Z"},"links":{"cited_paper":"/paper/1804.02767","citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:60f610697634aa64ee05dc566cddb2469e23feb0f2a9251afb2bd1db98a2e7be","observation_id":"0596d39d-8d03-4a6c-8c95-877dd66a1cde","resolution":{"observed_at":"2026-08-06T05:15:42.161362Z","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":"2026-08-06T05:15:42.204837Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.204837Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:65c76ebb108e2aff3379264591b362b7a22d8ea092b672a4f5fa09b9ceb4e3c7","observation_id":"2b863217-7df0-45cc-b70b-06f2fdfeab72","resolution":{"observed_at":"2026-08-06T05:15:42.204837Z","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":"2026-08-06T05:15:43.953945Z","title":"Focal loss for dense object detection,","venue":null,"work_id":"627763e4-26ac-4d99-afe5-0b520ae26cd3","year":2017},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.229101Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:1506c7d216780f20b1583a3d13f970235474c8c7a30055e3e94d5e74412003a6","observation_id":"165faf73-61a7-4c4a-8428-deeb652ca9c0","resolution":{"observed_at":"2026-08-06T05:15:43.963071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.10934","last_updated":"2020-04-23T02:10:02Z","snapshot_observed_at":"2026-07-06T09:14:32.318388Z","submitted_at":"2020-04-23T02:10:02Z","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.10934","snapshot_observed_at":"2026-08-06T05:15:42.275092Z","title":"Yolov4: Op- timal speed and accuracy of object detection,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.275092Z"},"links":{"cited_paper":"/paper/2004.10934","citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:add916abaf6e02d68d7da644ecf11a4312d9be0a4403380f94a21870bd92f4f2","observation_id":"66ef7014-5d59-4361-914a-ba698219c123","resolution":{"observed_at":"2026-08-06T05:15:42.275092Z","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":"2026-08-06T05:15:43.930781Z","title":"Cspnet: A new backbone that can enhance learning capability of cnn,","venue":null,"work_id":"85b8a592-ab59-4fcc-b225-ed12c99d51ef","year":2020},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.304395Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:e6902fe69eaf063c15832d651473ede881dbbb3c20e13359c39e66248938fcc4","observation_id":"0348b434-aba2-4860-af79-69107e73c7eb","resolution":{"observed_at":"2026-08-06T05:15:43.938578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:43.906311Z","title":"Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,","venue":null,"work_id":"7298ed2b-fcdc-45c3-a52a-5ee9cbf07fdd","year":2019},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.339186Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:286aa8c59b1fee3003f5f9fd960d00b1b345cce5b89514cac11a3deeed834b08","observation_id":"76735dc7-6d5a-4df7-9727-b13a8c976f4f","resolution":{"observed_at":"2026-08-06T05:15:43.915199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:43.881438Z","title":"Dropblock: A regularization method for convolutional networks,","venue":null,"work_id":"506e0701-105c-4d8c-a98b-794997cad38c","year":2018},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.373868Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:a34b8a4eb217fe2a35c9ffcc647dbbb04793bc7db8798fb15c1a5ab23bded3ee","observation_id":"f5ab99cf-bbda-4fdc-88f7-7c5d12e48522","resolution":{"observed_at":"2026-08-06T05:15:43.893920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.08681","last_updated":"2020-08-13T05:42:12Z","snapshot_observed_at":"2026-07-06T08:16:16.271286Z","submitted_at":"2019-08-23T06:22:06Z","title":"Mish: A Self Regularized Non-Monotonic Activation Function","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.08681","snapshot_observed_at":"2026-08-06T05:15:42.396390Z","title":"Mish: A self regularized non-monotonic activation function,","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.396390Z"},"links":{"cited_paper":"/paper/1908.08681","citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:b872665cf218dac1d97e81dc5bd0f35d48b5b6ef5a7b7e919dffa63efc348329","observation_id":"ac335f87-ce03-490d-91c3-3691910635d5","resolution":{"observed_at":"2026-08-06T05:15:42.396390Z","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":"2026-08-06T05:15:43.851789Z","title":"Spatial pyramid pooling in deep convolutional networks for visual recognition,","venue":null,"work_id":"5a8f8e17-7625-4dfc-a29b-fcbe158d0b52","year":2014},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.416466Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:e2b476abc62b22f5280c9c6a961b7611b83f600ddee58cf4139e8cbc5a62b40d","observation_id":"ed7ab9e7-aaa3-43ce-9984-2d693dae59c0","resolution":{"observed_at":"2026-08-06T05:15:43.865665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:43.826755Z","title":"Path aggregation network for instance segmentation,","venue":null,"work_id":"20189a2b-4a2a-43c6-bbc4-3f643a141380","year":2018},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.452365Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:4cd2fcbc74fea3bffcfc73e6879f7f87187fd91c7c3236dc879ecc6e0c258971","observation_id":"52f40dc0-4354-466c-bc9c-d7f07d0a0875","resolution":{"observed_at":"2026-08-06T05:15:43.832968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:42.482801Z","title":"ultralytics/yolov5: v1.0 - first release,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.482801Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:637ef36eeca4228ea6edecff5ea70a3991b7cd523e3d3d25f28fd519f8ec3a50","observation_id":"fbc3d595-9922-49f0-9a82-bfc1958f0fac","resolution":{"observed_at":"2026-08-06T05:15:42.482801Z","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":"2026-08-06T05:15:42.515997Z","title":"mixup: Beyond empirical risk minimization,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.515997Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:39c85bc99a5e6e1448d3aefb8a417569e220434f8a658cd388a24e2d6a14af31","observation_id":"f761f03a-bd27-4db1-8bea-cc3e5e12a13f","resolution":{"observed_at":"2026-08-06T05:15:42.515997Z","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":"2026-07-06T13:49:37.223049Z","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":"2026-08-06T05:15:42.553276Z","title":"Yolov6: A single-stage ob- ject detection framework for industrial applications,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.553276Z"},"links":{"cited_paper":"/paper/2209.02976","citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:28e33d9e185ba76587ac0787e345c3e27186da7d6b110308aa357d2387357ec8","observation_id":"1fbd0aca-e3d3-4a45-a9c7-a88e64cc72c6","resolution":{"observed_at":"2026-08-06T05:15:42.553276Z","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":"2026-08-06T05:15:43.799712Z","title":"Repvgg: Making vgg-style convnets great again,","venue":null,"work_id":"cc6badf7-3696-4712-8e6c-98fca2cd8f65","year":2021},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.595757Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:ff7c5bf1601faa93e4f773844fc7d1e0a29e9048581315e10cd4a46308728524","observation_id":"bccb482d-a474-408f-b600-9a0942ff04fb","resolution":{"observed_at":"2026-08-06T05:15:43.808634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:43.776901Z","title":"Fcos: Fully convolutional one-stage object detection,","venue":null,"work_id":"ba895006-5187-4c35-b030-fbd219bceea2","year":2019},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.628360Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:6a9f58990c3dbaee310a26f11a06e9cce1f01f26b699ff2c113615ed0a34ad72","observation_id":"262f79c4-79eb-44a0-9894-226c99aeed20","resolution":{"observed_at":"2026-08-06T05:15:43.786300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.02696","last_updated":"2022-07-06T14:01:58Z","snapshot_observed_at":"2026-08-06T14:09:02.699619Z","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":"2026-08-06T05:15:42.658238Z","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":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.658238Z"},"links":{"cited_paper":"/paper/2207.02696","citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:2115e371954621bbf79319cb983828094c3362892aed60daa0ddfd2591f8b5fe","observation_id":"d97101f4-83ad-4a0a-b906-b63a355abc68","resolution":{"observed_at":"2026-08-06T05:15:42.658238Z","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":"2026-08-06T05:15:43.749455Z","title":"Ultralytics yolov8,","venue":null,"work_id":"39a4d8d3-3e4a-434b-8d7e-bbd61daaee97","year":2023},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.696661Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:f8c5c2ea5ebf3e1b38327e545d2735407431361d586f1f760ac57564ff581241","observation_id":"e4b3e6d4-5857-462e-b3b5-defaa504abe9","resolution":{"observed_at":"2026-08-06T05:15:43.762997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.07850","last_updated":"2019-04-25T16:20:02Z","snapshot_observed_at":"2026-07-06T07:46:34.901111Z","submitted_at":"2019-04-16T17:54:26Z","title":"Objects as Points","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.07850","snapshot_observed_at":"2026-08-06T05:15:42.736080Z","title":"Objects as points,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.736080Z"},"links":{"cited_paper":"/paper/1904.07850","citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:4f4636598209c8c21648210c29ab4a0dba1bd194458e1c32ae25753c840b89ea","observation_id":"c1a65794-ed55-43f4-9f96-3fe244ffbc8a","resolution":{"observed_at":"2026-08-06T05:15:42.736080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13616","last_updated":"2024-02-29T03:43:24Z","snapshot_observed_at":"2026-08-06T07:09:56.727218Z","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":"2026-08-06T05:15:42.792878Z","title":"Yolov9: Learning what you want to learn using programmable gradient information,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.792878Z"},"links":{"cited_paper":"/paper/2402.13616","citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:4f38e47b440be3d6a1ad9a1edbe10dc45a9ca0211daed59a6eae42146aa294eb","observation_id":"8b03c36f-8739-4e35-aa1c-219790e8c1ad","resolution":{"observed_at":"2026-08-06T05:15:42.792878Z","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":"2026-08-06T05:15:43.720752Z","title":"Efficientdet: Scalable and efficient object detection,","venue":null,"work_id":"262476ea-5c9c-496f-bd66-7fab898f50b9","year":2020},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.830827Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:6a81186920cdffc4bc4985f65f970c7f9f598a6e7b5b2e73b76fe6c8cf5ad783","observation_id":"c01a34a6-81b9-4ac9-b34a-9594a9cb0838","resolution":{"observed_at":"2026-08-06T05:15:43.734050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:43.689841Z","title":"Ota: Optimal transport assignment for object detection,","venue":null,"work_id":"41239ebf-135e-4efb-ac08-ae3483cc8d31","year":2021},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.865761Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:56db626ee31d20e06aa716e8d76c69e33a149e4a6677cc81a77e09b1f8f6b169","observation_id":"06c0561a-2772-4858-b0e5-e738e9170e70","resolution":{"observed_at":"2026-08-06T05:15:43.704137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:43.666157Z","title":"Yolov11: Release notes and model overview,","venue":null,"work_id":"e5472997-598a-4125-ad8a-a7d82853cee4","year":2024},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.914187Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:d6ec364595e1be5139ef02fccc7d629ad7dc2b8d1a61c453e02e3999e8d2e9d3","observation_id":"68427eed-fa64-44c6-945b-2e9948cb0ce9","resolution":{"observed_at":"2026-08-06T05:15:43.675073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:43.642225Z","title":"Ultralytics yolov11 models: Comparison and performance,","venue":null,"work_id":"f2aeefe5-fbf5-4c43-ad56-93eb0e60cbe9","year":2024},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.961716Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:ccb0eef232fd5fa2e0ef067a4d696f5c392ddea1570e18da53468dc78fb874ae","observation_id":"7e42515e-40f6-4675-839e-8ff1971e2506","resolution":{"observed_at":"2026-08-06T05:15:43.654466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T05:15:43.466386Z","title":"Yolov11: Revolutionizing agricultural fruitlet detection with enhanced accuracy and real-time deployment,","venue":null,"work_id":"c92b15f8-45a1-4e7d-b943-f9cd26848118","year":2023},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:42.998635Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:eb113ff519bdd62c6629d2f5c702c7dfe1d3415627064d4e0e6e1a316e50508f","observation_id":"88eb9461-e6c5-43ef-8926-98882e1f3d8f","resolution":{"observed_at":"2026-08-06T05:15:43.479818Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:43.622403Z","title":"Domain adaptive yolo for cross-domain object detection,","venue":null,"work_id":"2b95b740-93ff-45bd-8846-6a227b7e334e","year":2022},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:43.032085Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:8e4cd75a2b1a824d96bc563f70bdb31cd3930f81dffbf9d468379ae7fb53b4ca","observation_id":"de3e049f-d2de-4da0-8aa7-704af77dadef","resolution":{"observed_at":"2026-08-06T05:15:43.631166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:43.592062Z","title":"Stac: Semi-supervised learning for object detection via strong-to-weak consistency,","venue":null,"work_id":"88757899-9530-4ed9-a0c4-ee0d2d2040b8","year":2021},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:43.057683Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:f36a04aff960981a8dbb6674d4b934d030e63bbdb822b1552e7d36f0c59d8c00","observation_id":"94768042-6818-418c-b263-675f55bd61a2","resolution":{"observed_at":"2026-08-06T05:15:43.604753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:15:43.571630Z","title":"Robust-yolo: Noise and occlusion aware object detection,","venue":null,"work_id":"da019949-b2c0-4370-8b2b-1a054eba47b9","year":2021},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:43.092552Z"},"links":{"citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:0d39cde2723374b09d4b63eafd9536cf0bb417f6a50e426fd53bea5a8331561d","observation_id":"7b802c87-2dd1-41d9-9594-9a6fe4825cdd","resolution":{"observed_at":"2026-08-06T05:15:43.575341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10117","last_updated":"2022-05-01T06:49:13Z","snapshot_observed_at":"2026-07-06T13:12:01.486619Z","submitted_at":"2022-05-01T06:49:13Z","title":"DDDM: a Brain-Inspired Framework for Robust Classification","version":1},"cited_work":{"arxiv_id":"2205.10117","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.10117","snapshot_observed_at":"2026-08-06T05:15:43.234546Z","title":"DDDM: a Brain-Inspired Framework for Robust Classification","venue":"cs.NE","work_id":"e8d2450c-a00a-4a96-a3fe-5872511083ec","year":2022},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:43.110969Z"},"links":{"cited_paper":"/paper/2205.10117","citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:03a2e61ec7780b51ba91461e61cf769cbcf36c685ec5c3f1fb8aaccd933f6a9f","observation_id":"df3cede8-681c-472a-bffa-402401aa5856","resolution":{"observed_at":"2026-08-06T05:15:43.253346Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.16199","last_updated":"2024-09-18T23:54:36Z","snapshot_observed_at":"2026-08-06T06:36:02.994951Z","submitted_at":"2023-03-28T17:59:12Z","title":"LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.16199","snapshot_observed_at":"2026-08-06T05:15:43.126280Z","title":"Yolo- nas: Neural architecture search for object detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:43.126280Z"},"links":{"cited_paper":"/paper/2303.16199","citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:fa5dd2d41c8f48e0811360b7c21cf162543d24ed1289a3a192a6938a12ae96df","observation_id":"ec9b9f7b-57e8-4025-83ab-c4e2b1bfcfa8","resolution":{"observed_at":"2026-08-06T05:15:43.126280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.09302","last_updated":"2022-07-19T14:35:42Z","snapshot_observed_at":"2026-07-06T13:32:57.557863Z","submitted_at":"2022-07-19T14:35:42Z","title":"Deep Semantic Statistics Matching (D2SM) Denoising Network","version":1},"cited_work":{"arxiv_id":"2207.09302","doi":null,"metadata_source":"pith","pith_arxiv_id":"2207.09302","snapshot_observed_at":"2026-08-06T05:15:43.188943Z","title":"Deep Semantic Statistics Matching (D2SM) Denoising Network","venue":"cs.CV","work_id":"b8122d92-7559-4048-a861-806c0a0c58b2","year":2022},"citing_paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T05:15:43.142692Z"},"links":{"cited_paper":"/paper/2207.09302","citing_paper":"/paper/2508.02067"},"observation_digest":"sha256:ad0ef17061adf894b9123d923f29629c272b78ec8fc5fda0dc5e1ead94ab07a8","observation_id":"35b441f4-b625-4bbe-8f4c-9a1799cece08","resolution":{"observed_at":"2026-08-06T05:15:43.204587Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.02067","last_updated":"2025-08-04T05:13:51Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T04:58:04.928079Z","submitted_at":"2025-08-04T05:13:51Z","title":"YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":14,"verified_exact":2,"verified_fuzzy":23},"total_outbound_references":40},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2508.02067."}