{"as_of":"2026-08-07T22:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:378a83203e5b7c6e7527ca97208ee8aa98120c5a7035cbee2458bae611ee55dc","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:34:36.371474Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2506.21135/citation-record","integrity":"/paper/2506.21135/integrity","json":"/paper/2506.21135/citation-record.json","paper":"/paper/2506.21135"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.09533","last_updated":"2025-02-13T17:50:23Z","snapshot_observed_at":"2026-08-07T22:10:29.240376Z","submitted_at":"2025-02-13T17:50:23Z","title":"Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.09533","snapshot_observed_at":"2026-08-06T22:34:32.776804Z","title":"Long-term talkingface generation via motion-prior conditional diffusion model,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:32.776804Z"},"links":{"cited_paper":"/paper/2502.09533","citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:bc8ca773d960bda0c1e06011547731a38ae3bec12080a7c5079a5a86573ab76b","observation_id":"e680273a-eb9a-4af6-b8b9-2035517f8423","resolution":{"observed_at":"2026-08-06T22:34:32.776804Z","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":"2026-08-06T22:34:32.878617Z","title":"Imagharmony: Controllable image editing with consistent object quantity and layout,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:32.878617Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:005244baeb1ad28fce09be0b9e27b79683949fb7860959f241155b88c6c31e54","observation_id":"d312cb52-27a0-407c-9ab6-e90f84cce23b","resolution":{"observed_at":"2026-08-06T22:34:32.878617Z","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":"2026-08-06T22:34:32.956875Z","title":"Rich feature hierarchies for accurate object detection and semantic segmentation,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:32.956875Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:e0a7d5a54fd06ff70c70b8931373a40f9b40a0813d8953834f82dba1c863cfc2","observation_id":"1df4e27a-d0c0-437e-a1ed-0ca28f242d51","resolution":{"observed_at":"2026-08-06T22:34:32.956875Z","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":"2026-08-06T22:34:43.163275Z","title":"Fast r-cnn,","venue":null,"work_id":"6997c3ec-74ea-4853-ba0f-1f6eb1667c28","year":2015},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.057812Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:fe2d336d0505b04203d08f8cbc5e815b03db962f25f9dc847218234f638fba59","observation_id":"22a26a6f-4e5f-4b0d-81c4-d479d63f449c","resolution":{"observed_at":"2026-08-06T22:34:43.261671Z","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":"2026-08-06T22:34:42.985802Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks,","venue":null,"work_id":"7dc531c2-1de1-4016-8d15-b1ffc1507a60","year":2015},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.133326Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:6dbcbd44d6e317adfa4b50d543fd1f2957d26e95460b604b6a99858efa342b62","observation_id":"27b806c9-0ca5-4203-8836-249a6970081e","resolution":{"observed_at":"2026-08-06T22:34:43.084400Z","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":"2026-08-06T22:34:33.203311Z","title":"Mask r-cnn,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.203311Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:2310855f560fc69ad9e0d153a2e4755d2e520d7951878704d550f642635cd043","observation_id":"032caaa6-45ca-4b12-92a8-380f16965dcd","resolution":{"observed_at":"2026-08-06T22:34:33.203311Z","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":"2026-08-06T22:34:42.785502Z","title":"Cascade r-cnn: Delving into high quality object detection,","venue":null,"work_id":"d85da459-6609-4524-b265-a287c4f58a63","year":2018},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.282548Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:d4848d7c2887bd8aa29050ae6f645ce4a545824fb9b0f3d1e3d908a1e7236f6d","observation_id":"b6c6663c-d28f-4127-a7a6-219ccd0b577a","resolution":{"observed_at":"2026-08-06T22:34:42.866019Z","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":"2026-08-06T22:34:42.566665Z","title":"Region proposal by guided anchoring,","venue":null,"work_id":"045311c9-547a-4d52-8787-967198dd8b41","year":2019},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.321823Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:b660acf5983b1d86a6625a4be86574399cd7192411cac8e9361a12dc759b19a2","observation_id":"213feb41-4564-495e-8850-65a9288ca32e","resolution":{"observed_at":"2026-08-06T22:34:42.686771Z","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":"2026-08-06T22:34:42.385035Z","title":"Cascade rpn: Delving into high-quality region proposal network with adaptive convolution,","venue":null,"work_id":"606580f3-ad93-4a74-8432-07978d1c74f7","year":2019},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.367072Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:26740e9db044259136149141d116cbd75399faac4041113d36cc8af611f70437","observation_id":"220c4f74-c731-4fd6-bb84-ec209cf1f29d","resolution":{"observed_at":"2026-08-06T22:34:42.493951Z","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":"2026-08-06T22:34:42.209730Z","title":"Ssd: Single shot multibox detector,","venue":null,"work_id":"3b4901f9-8586-4a69-973d-2b69bc9c0bc6","year":2016},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.449386Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:603033bcd6456883bc79d44880a94c68e578e193fff26f55cd959af6d9766d1e","observation_id":"6e2c9ea5-216c-463b-99a5-f2774e08c124","resolution":{"observed_at":"2026-08-06T22:34:42.285888Z","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":"2026-08-06T22:34:33.522708Z","title":"Yolov3: An incremental improvement,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.522708Z"},"links":{"cited_paper":"/paper/1804.02767","citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:4947a5874be53b1ba6a4535ad000365161490c16a721cb22bc25f25e6e6f068a","observation_id":"c991006b-e806-4c60-8702-9cd3728ff575","resolution":{"observed_at":"2026-08-06T22:34:33.522708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-06T22:34:33.599085Z","title":"Yolov4: Optimal speed and accuracy of object detection,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.599085Z"},"links":{"cited_paper":"/paper/2004.10934","citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:87906ec21b594a105403fb7e08a6d154e815fe588436ed0f550383dd986ee687","observation_id":"ac169ae5-06f4-4690-92cc-13f4fc77a37d","resolution":{"observed_at":"2026-08-06T22:34:33.599085Z","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":"2026-08-06T22:34:42.005402Z","title":"Yolov1 to v8: Unveiling each variant–a comprehensive review of yolo,","venue":null,"work_id":"3d826e9c-e3a8-44c3-b0b3-feb81afbf5e9","year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.643931Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:b77885e678b69b973b986a2da962b597de25295b7956995e9cab0309929b8864","observation_id":"474b8bb4-d804-40f5-a018-3c1fff16a85c","resolution":{"observed_at":"2026-08-06T22:34:42.108541Z","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":"2026-08-06T22:34:41.776611Z","title":"Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model,","venue":null,"work_id":"eee246f5-947e-4772-a47c-753258c96a02","year":2025},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.708327Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:170b48ead840636863e2502c644ca86ba9dbdf94e0895161e5f877efe7c243d5","observation_id":"eb88c53d-ffaa-4513-8377-75280d84867d","resolution":{"observed_at":"2026-08-06T22:34:41.887887Z","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":"2026-08-06T22:34:41.514317Z","title":"Aff-net: A strip steel surface defect detec- tion network via adaptive focusing features,","venue":null,"work_id":"402a8de8-e7fc-4fde-bf2a-140cb0aba967","year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.781625Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:6f5798ffe2f15c99c3b1dee8c7ede265d04b4dc8320fc83c6e158ad8eba3fed3","observation_id":"5a616715-2133-46f5-88d5-1ca7dc5dbc97","resolution":{"observed_at":"2026-08-06T22:34:41.636466Z","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":"2026-08-06T22:34:41.277132Z","title":"Multi-scale ship target detection using sar images based on improved yolov5,","venue":null,"work_id":"670d0f15-f101-4f41-b231-6a111df4bca2","year":2023},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.852788Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:5c203b414f60911790149bad6ab416f5edeb82dac3e71afccd1fcca0bc911ca5","observation_id":"d67709f9-26cf-4ca0-8a38-dc23bdd3483f","resolution":{"observed_at":"2026-08-06T22:34:41.367434Z","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":"2026-08-06T22:34:41.121820Z","title":"Yolo-lfpd: A lightweight method for strip surface defect detection,","venue":null,"work_id":"db7bdf80-d9c4-4eaf-8bf8-49a8b96fc443","year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.919624Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:e727d515019a1866026d39317b7e91eddf410fc1b6539c06308c40b321463288","observation_id":"6fde2d7f-49d9-4b96-9de3-c8a626dcc684","resolution":{"observed_at":"2026-08-06T22:34:41.191256Z","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":"2504.13176","last_updated":"2025-09-08T12:05:58Z","snapshot_observed_at":"2026-08-07T16:01:20.626786Z","submitted_at":"2025-04-17T17:59:47Z","title":"IMAGGarment: Fine-Grained Garment Generation for Controllable Fashion Design","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.13176","snapshot_observed_at":"2026-08-06T22:34:33.955681Z","title":"Imaggarment-1: Fine-grained gar- ment generation for controllable fashion design,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:33.955681Z"},"links":{"cited_paper":"/paper/2504.13176","citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:edf9d46c81e3c15acf0569b092cc964fb1dc68269918e30867b6fd5d7935f23b","observation_id":"2fbf55c6-c874-4111-a82a-8a07c61f124c","resolution":{"observed_at":"2026-08-06T22:34:33.955681Z","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":"2026-08-06T22:34:40.956700Z","title":"Imagdressing-v1: Customizable virtual dressing,","venue":null,"work_id":"b9fca846-6e25-46f3-978f-428c1a2edf11","year":2025},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.019944Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:dfc27db5fd9e5e0ab6f11c6e3437e9ffb692be5af5b121947d5312837b30c44b","observation_id":"65c440f7-1835-4c2b-abdb-b73471eb94bb","resolution":{"observed_at":"2026-08-06T22:34:41.013478Z","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":"2026-08-06T22:34:34.076519Z","title":"Imagpose: A unified conditional framework for pose-guided person generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.076519Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:f650fd5bc6a5b85078215d13ec3f1664df44c9887537e69ea4ab0097e09abca3","observation_id":"29dca472-ea31-41dc-9834-dd843a58242b","resolution":{"observed_at":"2026-08-06T22:34:34.076519Z","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":"2026-08-06T22:34:34.119220Z","title":"You only look once: Unified, real-time object detection,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.119220Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:7362594b706a074bb72347f99e32ddd052af36092800e841f21a4eaad9b5f62d","observation_id":"7b0f06b8-6ff1-4a23-9631-5babb5eb4301","resolution":{"observed_at":"2026-08-06T22:34:34.119220Z","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":"2026-08-06T22:34:40.699622Z","title":null,"venue":null,"work_id":"201db8f2-2d69-46f4-8e02-b032a153c650","year":null},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.172758Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:be22160609187e478e5f05892af5bfc393bc2301371bfd8fabead3ed2ecc049f","observation_id":"99313f2f-ad09-4227-bd90-1eeab338e47f","resolution":{"observed_at":"2026-08-06T22:34:40.820414Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-06T22:34:40.526588Z","title":"Yolo-world: Real-time open- vocabulary object detection,","venue":null,"work_id":"bc7854c4-882c-45e6-a5fb-71e80557d7e7","year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.239979Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:b54399ec5ea6b7c5a5079a38ed8895db1eebaf91fcd2dd527ab4e0403f4632e8","observation_id":"fea75711-897d-46ac-86b8-8f987518132f","resolution":{"observed_at":"2026-08-06T22:34:40.577789Z","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":"document/1075289","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:34:36.670711Z","title":"QCF-YOLO: A lightweight model of surface defect detection for quick-connect fittings,","venue":null,"work_id":"5d3631db-5009-474d-b292-5dc45a1ba099","year":2025},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.325491Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:fde235bf67d8d1c7877d2aaf97c7da43d7e593468808719a00654c82cd87401d","observation_id":"e7f5914b-9d0d-48a9-89ea-a8b05ba4168d","resolution":{"observed_at":"2026-08-06T22:34:36.721549Z","resolver_source":"raw_fallback","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":"2026-08-06T22:34:40.337828Z","title":"A novel cross frequency-domain interaction learning for aerial oriented object detection,","venue":null,"work_id":"8989b757-c4d4-4bf9-aa8c-4e92c9cf9cea","year":2023},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.368942Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:c1803ab4778afcbdca77a46b972676c835af858ef8fef14ef89c4d9f76a432f0","observation_id":"7da1341f-f1f9-4589-8ce5-ba8c2d904c27","resolution":{"observed_at":"2026-08-06T22:34:40.408734Z","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":"2026-08-06T22:34:40.140462Z","title":"A novel multi-frequency coordinated module for sar ship detection,","venue":null,"work_id":"f4322c11-db54-46c9-8287-ed38d326c073","year":2022},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.428480Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:fbfb2cecf2ebc80aa3f2fc87c6f860027410f2d291f988f6670fc12582568a66","observation_id":"17bf6ef8-ea4e-4a5e-b5df-c122100bb3a1","resolution":{"observed_at":"2026-08-06T22:34:40.229966Z","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":"2026-08-06T22:34:34.489412Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.489412Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:19f8fc90bd5fbd55a49ab11ff39f4b070218ba8fd6032f4d81d58528c9be406b","observation_id":"3c3fde1d-88d1-46f0-a6b1-b692c5f3fd36","resolution":{"observed_at":"2026-08-06T22:34:34.489412Z","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":"2026-08-06T22:34:34.551983Z","title":"Squeeze-and-excitation networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.551983Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:1a43f33a99a36556acd88b1c7225e7a135c9d26e2919a054046cf0256631072a","observation_id":"fe839587-f2c5-46cc-ab3d-5b35dc48b09e","resolution":{"observed_at":"2026-08-06T22:34:34.551983Z","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":"2026-08-06T22:34:39.914063Z","title":"Cbam: Convolutional block attention module,","venue":null,"work_id":"55372896-af24-4858-8f38-9e4f0f8830b7","year":2018},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.610229Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:05ccbd9783cf3f0e2b67268672a536bfc9bc92995db8a5ef96decc4d7ddefcb8","observation_id":"80d7d8f8-4989-4921-8892-51ad42969ae4","resolution":{"observed_at":"2026-08-06T22:34:39.998772Z","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":"2026-08-06T22:34:39.691348Z","title":"Yolo-hmc: An improved method for pcb surface defect detection,","venue":null,"work_id":"3596b9a8-b13b-4537-9d56-da6479b17a3d","year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.682990Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:e06687d1a01904263499cc44d051297fbf8b603c0c3ff6bbb475f7d59fff38ec","observation_id":"9adc57c9-fb2f-4d5d-8d27-df81c98ea4ce","resolution":{"observed_at":"2026-08-06T22:34:39.785249Z","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":"2026-08-06T22:34:39.564579Z","title":"Enhancing aerial object detection with selective frequency interaction network,","venue":null,"work_id":"f7fe0c92-a9f6-4b9f-9635-7031876faa65","year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.755955Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:cd8783e1ed161aba03090e3db0b3276647b1aeececbe2118faf12109c6f5175d","observation_id":"62159c57-23d8-4436-96b6-2fe9e3136aa4","resolution":{"observed_at":"2026-08-06T22:34:39.638454Z","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":"2404.01614","last_updated":"2024-04-02T03:36:07Z","snapshot_observed_at":"2026-07-06T17:54:19.921202Z","submitted_at":"2024-04-02T03:36:07Z","title":"LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01614","snapshot_observed_at":"2026-08-06T22:34:34.819159Z","title":"Lr-fpn: Enhancing remote sensing object detection with location refined feature pyramid network,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.819159Z"},"links":{"cited_paper":"/paper/2404.01614","citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:37299bc50a8fd1551c97e8d8a27ae27e97a8af2f31d92d894539d7c9c2a8db58","observation_id":"7e4e632d-eecb-45b8-876e-ec0aa8a54c85","resolution":{"observed_at":"2026-08-06T22:34:34.819159Z","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":"2026-08-06T22:34:39.343032Z","title":"dataset., in : https://github.com/lvxiaoming2019/GC10-DET-metallic-surface-defect- datasets","venue":null,"work_id":"dd956f54-271b-4fb2-b484-6862c17a1cc8","year":null},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.879874Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:84e725168e3021f904b6d5fbac1e0cc48313d5040a95b910f349b7430dc9602d","observation_id":"ba2d345a-c5e8-4330-92b8-c86aca247198","resolution":{"observed_at":"2026-08-06T22:34:39.448898Z","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":"2026-08-06T22:34:39.151333Z","title":"Weakly supervised learning of a classifier for unusual event detection,","venue":null,"work_id":"abe0cbdd-10a2-4224-92ff-8ae4ac2cd19e","year":2008},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.885372Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:6b630c840742feabd43cf5a654f8bba4cc5163d7ee35be266276b28b6670da5b","observation_id":"8c37ceee-6766-4402-b087-8ec0b487ce2b","resolution":{"observed_at":"2026-08-06T22:34:39.250427Z","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":"2026-08-06T22:34:38.969986Z","title":null,"venue":null,"work_id":"c572d961-4dc6-490a-9c46-6f4efbd7b13a","year":2023},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:34.958861Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:4c40e7268c7ada39a49d60f57bc98af36b8c4854a15dc276661789389e31b824","observation_id":"4bd9d164-1e3c-4237-8599-4b76a261dbd3","resolution":{"observed_at":"2026-08-06T22:34:39.038349Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-06T22:34:38.766609Z","title":"Joining spatial deformable con- volution and a dense feature pyramid for surface defect detection,","venue":null,"work_id":"bb6c9aab-61f3-4102-af8c-42c1b4f3cc17","year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:35.157410Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:f49b253a51dcbbb9681031240d93d824a5720db4cbb4678077ce6ba8c0a11891","observation_id":"abe1dcf1-fed8-48e9-b5ba-acef6c12a275","resolution":{"observed_at":"2026-08-06T22:34:38.862937Z","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":"2026-08-06T22:34:38.518590Z","title":"Es-net: Efficient scale-aware network for tiny defect detection,","venue":null,"work_id":"cd9d5047-6570-4217-b960-b7f3733e0c36","year":2022},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:35.315237Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:4c1a32649dafd23ab9b6ab9a5de7623101ba0cebab06ff02a4ab4abcd38ac5b0","observation_id":"dc137ecf-67fe-4d22-a424-e97f5456ec87","resolution":{"observed_at":"2026-08-06T22:34:38.623751Z","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":"2026-08-06T22:34:38.348189Z","title":"Cspnet: A new backbone that can enhance learning capability of cnn,","venue":null,"work_id":"8951088b-d3bc-4398-8ba7-4a72cd32fcbc","year":2020},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:35.508747Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:8aff7fc2c7bbfc554a44a8772d7cecb373dd9ccec67e1b43994dab5e3af5f6ff","observation_id":"fc21f06c-0083-4a08-a4df-d383b4409cc1","resolution":{"observed_at":"2026-08-06T22:34:38.413945Z","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":"2026-08-06T22:34:38.264325Z","title":"Spatial pyramid pooling in deep convolutional net- works for visual recognition,","venue":null,"work_id":"86cec722-b1d2-43d3-bfaf-d0370b5eeac7","year":1904},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:35.683881Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:b70eda995b23acdb85769c8ae4f0f573e7aaf6e4c1dedb7f308ce0473b9a433e","observation_id":"9018adfe-f1f2-4d21-9b11-0dea78476a88","resolution":{"observed_at":"2026-08-06T22:34:38.311835Z","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":"2026-08-06T22:34:38.145919Z","title":"Research on a metal surface defect detection algorithm based on dsl-yolo,","venue":null,"work_id":"3db16e1a-5273-48ef-affe-f90e8a9ee42b","year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:35.747948Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:190155d04cbd574c897a5966091518b2d3b1723e57cc098cedd87c5aaa144234","observation_id":"34d19331-75bc-4337-8e01-f3f665f9b852","resolution":{"observed_at":"2026-08-06T22:34:38.186118Z","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":"2026-08-06T22:34:38.013086Z","title":"Steel surface defect detection based on multi-layer fusion networks,","venue":null,"work_id":"d0eb782a-a8bc-4133-b42b-a8be0604aa98","year":2025},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:35.786003Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:c9528b12961dff08be3fcb37da74af57884559efc6acb3df0cef200fb1c0d93e","observation_id":"82de762e-db7b-4ab9-a5dc-8302e8a30720","resolution":{"observed_at":"2026-08-06T22:34:38.066748Z","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":"2026-08-06T22:34:37.829521Z","title":"Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,","venue":null,"work_id":"09fa7698-f3a4-4c9a-bd47-7b49ddc77b6b","year":2023},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:35.868198Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:a3f6f5fee48cf86b86cfe866f168a4ff3ba4a3033babe22e25724949be61d549","observation_id":"c75f10e2-ed34-427e-949c-b226c965cb64","resolution":{"observed_at":"2026-08-06T22:34:37.917692Z","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":"2026-08-06T22:34:37.682251Z","title":"Object detection method for grasping robot based on improved yolov5,","venue":null,"work_id":"772064c0-b92e-45e6-bf56-bea01391a92f","year":2021},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:35.977224Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:259bdf9140895355124cdee5a77a88c87a664606ea30c0d7779ff8364a159271","observation_id":"30010bc7-65af-41ca-8594-45c592d298ac","resolution":{"observed_at":"2026-08-06T22:34:37.747454Z","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":"2026-08-06T22:34:37.525503Z","title":"Steel surface defect detection based on mobilevitv2 and yolov8,","venue":null,"work_id":"261f7853-c0ce-4cce-8b9c-127f31516c30","year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:36.126837Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:633ee8d53a678e0e0a0487c05da39d85f49bb3d61831e694b8939338d8aa36af","observation_id":"44dec669-b7e5-4245-9b71-9c107d28fef3","resolution":{"observed_at":"2026-08-06T22:34:37.607142Z","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":"2026-08-06T22:34:37.403604Z","title":"Msb r-cnn: A multi-stage balanced defect detection network,","venue":null,"work_id":"c585f15f-ef8b-4f29-8d71-887f8199d8bf","year":1924},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:36.182930Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:b543c5f63306fd218e4c553b0dcf59a661c9c0972e50930dae6627e4c624bb16","observation_id":"895d18ff-a6e1-48d7-9eb3-5acd25cd838c","resolution":{"observed_at":"2026-08-06T22:34:37.471408Z","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":"2026-08-06T22:34:37.238620Z","title":"Hic-yolov5: Improved yolov5 for small object detection,","venue":null,"work_id":"3100e695-56af-4cec-b3f2-2234e327ad97","year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:36.286858Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:ed00fb257564d91993f849cd6394484231577846f76050b58b113f9262f552e6","observation_id":"2d24a406-2208-4a05-ab67-7b84553c46e9","resolution":{"observed_at":"2026-08-06T22:34:37.300188Z","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":"2026-08-06T22:34:37.079451Z","title":"Chained cascade network for object detec- tion,","venue":null,"work_id":"cb50baa4-3f0c-41b3-8277-9aca7adb8f9a","year":2017},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:36.339187Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:ed4da48256c9fd431f2febc403daa1696ce54e77435b7197faea97524bd0a97c","observation_id":"ee1594cd-32c9-4f64-8ba0-24bb207c2421","resolution":{"observed_at":"2026-08-06T22:34:37.143324Z","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":"2026-08-06T22:34:36.963426Z","title":"Yolov9: Learning what you want to learn using programmable gradient information,","venue":null,"work_id":"4202d143-bd1c-4bbc-a5d2-e32986d4363e","year":2024},"citing_paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T22:34:36.371474Z"},"links":{"citing_paper":"/paper/2506.21135"},"observation_digest":"sha256:286eb5201344f25c6d938ecfd20e073fb00b7f8c43aafdd43334408ce2c644d9","observation_id":"a656a4f0-5214-4537-bd3a-c183a74b9410","resolution":{"observed_at":"2026-08-06T22:34:36.990651Z","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"}}],"paper":{"arxiv_id":"2506.21135","last_updated":"2025-06-26T10:32:37Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T22:11:54.617341Z","submitted_at":"2025-06-26T10:32:37Z","title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":1,"verified_fuzzy":33},"total_outbound_references":48},"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 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2506.21135."}