{"as_of":"2026-08-06T00:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8789693ea7dd6ec457b527c993b13607fd2dc22e997e67e969fcb3516881407e","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T18:11:54.801912Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+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":"2026-07-11T23:03:47.227656Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.07759","snapshot_observed_at":"2026-07-11T23:03:47.227656Z","title":"arXiv preprint arXiv:2604.07759 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.03915","last_updated":"2026-07-08T04:06:17Z","snapshot_observed_at":"2026-08-03T00:23:14.004330Z","submitted_at":"2026-07-04T15:18:22Z","title":"NavEYE: Vision-Centered Multi-Sensor Fusion-Based Situational Awareness System for Intelligent Surface Vehicles","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-07-11T23:03:47.227656Z"},"links":{"cited_paper":"/paper/2604.07759","citing_paper":"/paper/2607.03915"},"observation_digest":"sha256:037775c8a51df62666814ed435f4d98024be67275c917903432daf48c234972b","observation_id":"7f682b3c-1dd7-4069-9f2b-a083b78d1636","resolution":{"observed_at":"2026-07-11T23:03:47.227656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2604.07759/citation-record","integrity":"/paper/2604.07759/integrity","json":"/paper/2604.07759/citation-record.json","paper":"/paper/2604.07759"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Maritime environment perception based on deep learning","venue":null,"work_id":"80b96c5c-06d9-48f3-98b5-1902bbc2e42a","year":2022},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:d903dad9e5473580935eccf42e0310d88b2f15a391243c57804599af4b5a1435","observation_id":"8db45c19-eaf2-474f-a513-764978f74f1b","resolution":{"observed_at":"2026-05-17T06:39:12.063836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A guide to image-and video-based small object detection using deep learning: Case study of maritime surveillance","venue":null,"work_id":"3f31eaad-2bd7-4dd3-9504-6b79d0865290","year":2025},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:054a09eaea8d09048a0680b6f23102984283aab0da86e669da637362496ca022","observation_id":"2f31dfe1-e7b7-4915-b956-749569d99b25","resolution":{"observed_at":"2026-05-17T06:39:12.053483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Mdd-shipnet: Math-data integrated defogging for fog-occlusion ship detection","venue":null,"work_id":"b6c93936-e615-4038-bb9d-ddc5ee0af8f2","year":2024},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:02c884fe055b217ad431566d472f043e11e892860527ec357e22fc7002e480f1","observation_id":"e49ff2d9-a45a-430e-8072-574d087e5b7e","resolution":{"observed_at":"2026-05-17T06:39:12.042803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Deep-learning-empowered visual ship detection and tracking: Literature review and future direc- tion","venue":null,"work_id":"e59bfae0-0e2f-4129-b1b2-d8f234f39883","year":2025},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:31862d5159eecde54a22b2cab6d17583f4acade78a65d8ce80003dd27ac57fd7","observation_id":"582165e6-328a-4efa-8ced-8821ce98e4e1","resolution":{"observed_at":"2026-05-17T06:39:12.153978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Aodemar: Attention-aware occlusion detection of vessels for maritime autonomous surface ships","venue":null,"work_id":"5b2d5280-aa26-4c3d-8c4a-3705fa47877a","year":2024},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:8de08f5547339f142a8b00b809678263f52587dc249f55456ac79372004288e1","observation_id":"631aff48-8683-4907-a650-827d8fe36d8d","resolution":{"observed_at":"2026-05-17T06:39:12.137396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks","venue":null,"work_id":"3075585d-4a25-4173-9fe8-5e6755a2e37f","year":2015},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:4f473ccc2c8a63494b463d0f92417c6a4c8d4bc8160d9d48bbd36a8e0e7c5ee7","observation_id":"e8e76f03-e296-4f59-9555-871c6c3285b8","resolution":{"observed_at":"2026-05-17T06:39:12.103592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.02767","last_updated":"2018-04-08T22:27:57Z","snapshot_observed_at":"2026-08-03T07:14:36.699933Z","submitted_at":"2018-04-08T22:27:57Z","title":"YOLOv3: An Incremental Improvement","version":1},"cited_work":{"arxiv_id":"1804.02767","doi":"10.48550/arxiv.1804.02767","metadata_source":"pith","pith_arxiv_id":"1804.02767","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"YOLOv3: An Incremental Improvement","venue":"cs.CV","work_id":"d737b3cc-9bd1-43d6-8310-c91e64b510f7","year":2018},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/1804.02767","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:dc515c6a633ac5621fd201f55643981fb41bfb444a8f3df4bc8e423d5dcfb401","observation_id":"d7ecfda3-24ff-49ec-970e-e3770c87c376","resolution":{"observed_at":"2026-05-13T11:37:52.054572Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-12T23:51:10.097273+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T23:51:10.097273+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2004.10934","doi":"10.3390/s22052341","metadata_source":"pith","pith_arxiv_id":"2004.10934","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","venue":"cs.CV","work_id":"7057aaee-27f6-4209-a83c-f59727f937a8","year":2020},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/2004.10934","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:20c56f8b878df00decf2984e4f476e7e63e00332e0520a6706134a2e104789f1","observation_id":"813a9318-a161-44cb-9f31-c6471bf7cc65","resolution":{"observed_at":"2026-05-12T14:34:24.827997Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Ssd: Single shot multibox detector","venue":null,"work_id":"66d8b098-4d92-4740-a8bc-e9aa16ca52a9","year":2016},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:1624da6a0852bdef760b4b8b6ff4dcdb05b27eb3fe27a0ba36181286b8b54455","observation_id":"b2e5b741-63fc-4104-a679-822fa0431a09","resolution":{"observed_at":"2026-05-17T06:39:12.071016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Attention is all you need","venue":null,"work_id":"fc819e21-af9c-40e9-b00b-a7fe90d4e58e","year":2017},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:ebc582a72bc4830f927f661bd6c933af82ede093d166c2cb896ea71968fa6611","observation_id":"e1f0d0ce-3de4-4fb4-9ad9-6b1b6e3776fa","resolution":{"observed_at":"2026-05-17T06:39:12.144142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":"2010.11929","doi":"10.1175/jcli-d-22-0357.1","metadata_source":"pith","pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","venue":"cs.CV","work_id":"e96730e3-129b-4db6-b981-15ab7932e297","year":2020},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:ef1bdcfe833e5861a645528cb1595cb72eb06b48e9135beb185464e22c3e1113","observation_id":"ff6a5bc3-ddb4-4aa4-b7c1-bd22bbf43ab2","resolution":{"observed_at":"2026-05-11T05:20:59.730698Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"End-to-end object detection with transformers","venue":null,"work_id":"74eb095e-9267-458e-bf9a-160e9d8d75f9","year":2020},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:30f37e1663e0811ab95ed2d86cba16b7be39cc843886f31d0fc26c7c58d28e95","observation_id":"88cbe44a-2ebf-48d5-b286-8dccceb86c36","resolution":{"observed_at":"2026-05-17T06:39:12.165001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04159","last_updated":"2021-03-18T03:14:26Z","snapshot_observed_at":"2026-07-06T10:02:45.105181Z","submitted_at":"2020-10-08T17:59:21Z","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","version":4},"cited_work":{"arxiv_id":"2010.04159","doi":"10.48550/arxiv.2010.04159","metadata_source":"pith","pith_arxiv_id":"2010.04159","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","venue":"cs.CV","work_id":"876f9fe8-c712-4550-9c26-cc18ab69abf2","year":2020},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/2010.04159","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:53acd013902de118c86e3f1f60cfe640977a254925b8d658447a9e865d740865","observation_id":"d32c49c3-0703-4219-9578-8e130bacf11e","resolution":{"observed_at":"2026-05-11T09:47:17.069034Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Detrs beat yolos on real-time object detection","venue":null,"work_id":"30028256-d778-4451-a1ac-a1b63cee5fed","year":2024},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:e3c07b940998ddb3742a2806508e94e32381071379c589ada4ca1b4aa2c270f2","observation_id":"c1a88f92-137e-4c6e-853c-1efde695adbd","resolution":{"observed_at":"2026-05-17T06:39:12.067445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Mamba: Linear-time sequence modeling with selective state spaces","venue":null,"work_id":"b079e620-3874-46ad-96b5-bf079eb41373","year":2024},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:c09b0a81aace5bdf68c7c4b766edc0d2515bb11f8faa15b6f56a25ccd492de50","observation_id":"fe37ec26-13ee-4115-a5c7-ebe60eb7a5bb","resolution":{"observed_at":"2026-05-17T06:39:12.060476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09417","last_updated":"2024-11-14T02:00:33Z","snapshot_observed_at":"2026-07-06T17:16:59.193820Z","submitted_at":"2024-01-17T18:56:18Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","version":3},"cited_work":{"arxiv_id":"2401.09417","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.09417","snapshot_observed_at":"2026-07-04T19:30:07.531920Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","venue":"cs.CV","work_id":"bd81352e-a64f-4720-9f76-ddda0ea9af83","year":2024},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:b7850e2b77fcea27dbc4ea73be1657214f2253384d2e49f17240cad26af1e065","observation_id":"23c1dac1-a225-403d-86ce-cbd0b0e7b626","resolution":{"observed_at":"2026-05-11T21:37:00.904611Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Seaships: A large-scale precisely annotated dataset for ship detection","venue":null,"work_id":"a54c6610-a4ae-45e7-bd2f-c18009095a9d","year":2018},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:f890f48c27820d014f66bd605ecbe677edaf2a62a7a0493c416ffba1a6e41d80","observation_id":"51a0bee3-f234-4195-9820-535581f821b5","resolution":{"observed_at":"2026-05-17T06:39:12.107044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"An image-based benchmark dataset and a novel object detector for water surface object detection","venue":null,"work_id":"fdf6b342-f7b3-47f9-b21e-c3faac05afff","year":2021},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:35ef6016ba6572b66b970f938b04f4206a1de2ed60aee6bd0d328196836e2377","observation_id":"bffa395a-e0b5-4268-aa5b-611c01a47e89","resolution":{"observed_at":"2026-05-17T06:39:12.046522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Waterscenes: A multi-task 4d radar- camera fusion dataset and benchmarks for autonomous driving on water surfaces","venue":null,"work_id":"2e613c18-58b3-4ebe-b508-82c3d7ca10fd","year":2024},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:4326190bd803fb6111ac7b7ff87612dfc1ae075af0df63494824aa7ad5bbc167","observation_id":"ecbbe8cc-db86-4c91-8b32-504689c3b189","resolution":{"observed_at":"2026-05-17T06:39:12.087348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey","venue":null,"work_id":"5d88deae-c2d8-4c65-b617-52fd43886d1d","year":1993},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:0ca6ecfa8c942f2a9a161fc925182f9d49ee241977e6b46f994dff39749fffae","observation_id":"31d899ec-9ceb-4f9d-8552-498c30e2c887","resolution":{"observed_at":"2026-05-17T06:39:12.056888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Mcships: A large-scale ship dataset for detection and fine-grained categorization in the wild","venue":null,"work_id":"c1e110a1-82b5-498b-9fb3-d37b7d02e2de","year":2020},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:162ffde6af1d48221b288bd94fef915f36ce29c70014be002aab9566b22e6665","observation_id":"b33c3a87-7cc4-4e4c-856a-c376e2856600","resolution":{"observed_at":"2026-05-17T06:39:12.049824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Simuships-a high resolution simulation dataset for ship detection with precise annotations","venue":null,"work_id":"2c8db0ac-ea5a-41bf-a7b9-f412dd8536ae","year":2022},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:f2630b1ea0f86486cff1e6b02902b399640f83b931aefb20d216e4ce0f50156a","observation_id":"a2695576-ef58-4a34-b23c-1873cd9509d1","resolution":{"observed_at":"2026-05-17T06:39:12.100678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Asynchronous trajectory matching-based multimodal maritime data fusion for vessel traffic surveillance in inland waterways","venue":null,"work_id":"7ceeea73-0055-4bc5-93b0-7fe2de0a027a","year":2023},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:8947eb4830a9a1000be3f0490ca157d81e4efa79afef106d7917adacf849165b","observation_id":"2b685f3f-ac10-4ef4-a1b5-db35dce9c4c8","resolution":{"observed_at":"2026-05-17T06:39:12.168743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Marine vessel detection dataset and benchmark for unmanned surface vehicles","venue":null,"work_id":"1bd48ef9-561f-4a85-8ab6-48ff50d293bd","year":2024},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:ba7b49691ab3e8def0dccc27bc60f3bfd87d8a83aa0773b83328e24e1484350b","observation_id":"391f68f0-0ae3-41fb-812f-45e54dc77b3f","resolution":{"observed_at":"2026-05-17T06:39:12.147616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"The pascal visual object classes (voc) challenge","venue":null,"work_id":"419146a4-7b6c-468e-bb99-f1f38074b20a","year":2010},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:866dd1d191d0f841e104a3b065866e0c8f355cc87675dfc2411be0d48a4871e4","observation_id":"d8231d7c-cdad-47ff-9b52-c44dbd587f22","resolution":{"observed_at":"2026-05-17T06:39:12.134116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"4edaecba-2746-48ec-9525-d568cb4d638e","year":2009},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:2beba7574f362aea5ae24dbabd7d4dd24a69d1020c0ab83985a08faa82a0a70d","observation_id":"7718fa3f-6dc1-4008-9e73-21cdc1d591c5","resolution":{"observed_at":"2026-05-17T06:39:12.140890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Imagenet large scale visual recognition challenge","venue":null,"work_id":"68aeaa44-43b1-4b73-97fa-d6fbb6783fa4","year":2015},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:1f897d503635000747b8e4c2e4f50c129f3e0bde6f1c7bf89291617511c1d9d6","observation_id":"c34e387d-3105-4399-a933-e414c4bcab60","resolution":{"observed_at":"2026-05-17T06:39:12.150847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":"5fd24e91-f4d8-41c2-9040-f85c3fea61a8","year":2014},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:0925d069fb015e1c8a852f84d347d497824bb15d505340b53eb1eaae972c9c62","observation_id":"402b0d9a-7f8f-4d3f-b962-0b9d22e6fd0a","resolution":{"observed_at":"2026-05-17T06:39:12.161085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale","venue":null,"work_id":"38d336e5-e29c-498c-96f3-82a4d0f80e31","year":1956},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:07c35b806eaded557e6bdad29fd5ce916708d519c70c7e667d17d08f7a74df63","observation_id":"1fd8615e-6462-4d90-87ab-cb0891aed7b8","resolution":{"observed_at":"2026-05-17T06:39:12.113891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Objects365: A large-scale, high-quality dataset for object detection","venue":null,"work_id":"16c087c8-b547-4031-aa5b-04237e93baa9","year":2019},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:a1756e8ebee830e10063be9f7e7d788b4aec3221bc88b97a4da2a94a26148045","observation_id":"2f0066a4-290c-49cf-bd1a-33f33a1c08ce","resolution":{"observed_at":"2026-05-17T06:39:12.110465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Fast image-based ob- stacle detection from unmanned surface vehicles","venue":null,"work_id":"f2b74696-83c7-486b-a2a8-f48202d9a406","year":2015},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:0d5ebad4c9684e58b2c63b567c705aae74baf7ee65c7fc4ab928159ee23c3143","observation_id":"f0c2a550-27ee-4b94-bc48-b0baa39b072f","resolution":{"observed_at":"2026-05-17T06:39:12.120653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Cascade r-cnn: Delving into high quality object detection","venue":null,"work_id":"2afa1ec0-cab1-4cad-ae0e-ea7acc92dc3c","year":2018},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:d3d61e265ee3235a49ad36aade10f6048e40ccef8f8bddb82be6b62b4c463f85","observation_id":"39bbaa1d-1346-4e94-8cca-1b689ca06019","resolution":{"observed_at":"2026-05-17T06:39:12.127298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.08430","last_updated":"2021-08-06T03:22:14Z","snapshot_observed_at":"2026-07-06T11:30:06.143581Z","submitted_at":"2021-07-18T12:55:11Z","title":"YOLOX: Exceeding YOLO Series in 2021","version":2},"cited_work":{"arxiv_id":"2107.08430","doi":null,"metadata_source":"pith","pith_arxiv_id":"2107.08430","snapshot_observed_at":"2026-07-04T19:20:06.601231Z","title":"YOLOX: Exceeding YOLO Series in 2021","venue":"cs.CV","work_id":"112b3cd9-8fe6-49fe-bbaa-90a3f46045c7","year":2021},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/2107.08430","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:4e3b329c89a5becd8ea7da4fd3a658666d7a79088a16379b9fd1d5cb0e39a3c5","observation_id":"5047207f-3fc8-49b7-b3c3-e6a6fbcad5f4","resolution":{"observed_at":"2026-05-13T10:31:31.716715Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12524","last_updated":"2025-02-18T04:20:14Z","snapshot_observed_at":"2026-07-06T20:38:20.545914Z","submitted_at":"2025-02-18T04:20:14Z","title":"YOLOv12: Attention-Centric Real-Time Object Detectors","version":1},"cited_work":{"arxiv_id":"2502.12524","doi":"10.48550/arxiv.2502.12524","metadata_source":"pith","pith_arxiv_id":"2502.12524","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"YOLOv12: Attention-Centric Real-Time Object Detectors","venue":"cs.CV","work_id":"b2da2ba7-83b7-4573-96e5-d6f2b8ef3897","year":2025},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/2502.12524","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:d5715115c84559323af3f348bcf0bfaca5a86e62b3009ab551ce1a8b19916406","observation_id":"7868e0c1-3a0c-4541-bbcd-41a1af8b432e","resolution":{"observed_at":"2026-05-13T21:35:11.362323Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.17733","last_updated":"2025-09-05T11:07:37Z","snapshot_observed_at":"2026-08-03T00:40:11.609117Z","submitted_at":"2025-06-21T15:15:03Z","title":"YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception","version":2},"cited_work":{"arxiv_id":"2506.17733","doi":null,"metadata_source":"pith","pith_arxiv_id":"2506.17733","snapshot_observed_at":"2026-07-08T01:14:27.454682Z","title":"Yolov13: Real-time object detection with hypergraph-enhanced adaptive visual perception","venue":"cs.CV","work_id":"1284df5c-671f-4303-a49b-643c4bc135f5","year":2025},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/2506.17733","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:3a432372a7f0143d13b479ae1b34cffce76a7ecd2ce3d4f03ade14d51accb931","observation_id":"0e771bb4-38e5-4eab-916b-8bca1df9d884","resolution":{"observed_at":"2026-05-11T05:20:59.707290Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12329","last_updated":"2022-03-30T16:04:38Z","snapshot_observed_at":"2026-08-03T22:59:50.390996Z","submitted_at":"2022-01-28T18:51:09Z","title":"DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR","version":4},"cited_work":{"arxiv_id":"2201.12329","doi":"10.48550/arxiv.2201.12329","metadata_source":"pith","pith_arxiv_id":"2201.12329","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dab-detr: Dynamic anchor boxes are better queries for detr","venue":"cs.CV","work_id":"2e01e759-de8e-43c2-9c99-f49e3edcb179","year":2022},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/2201.12329","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:d922edc042fee6ffc6e8d0f1488c64069cc065599ba82da324cc47781f164068","observation_id":"369e33e8-74a1-44c9-9474-09f81f5d44db","resolution":{"observed_at":"2026-05-11T05:20:59.755818Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.03605","last_updated":"2022-07-11T10:30:29Z","snapshot_observed_at":"2026-08-04T22:53:41.491205Z","submitted_at":"2022-03-07T18:55:26Z","title":"DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection","version":4},"cited_work":{"arxiv_id":"2203.03605","doi":"10.48550/arxiv.2203.03605","metadata_source":"pith","pith_arxiv_id":"2203.03605","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection","venue":"cs.CV","work_id":"4e842f69-fa99-4dde-b8a7-b5a558d4c80b","year":2022},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/2203.03605","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:f45f299b1e1d2094d504a3f69fdda5321bddb7946bd95132a8384a38deecd7b3","observation_id":"15ddb013-2d08-4de1-bbb0-a75ca83d821c","resolution":{"observed_at":"2026-05-12T19:53:25.827023Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.13842","last_updated":"2024-10-17T17:57:01Z","snapshot_observed_at":"2026-07-06T19:35:30.732080Z","submitted_at":"2024-10-17T17:57:01Z","title":"D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement","version":1},"cited_work":{"arxiv_id":"2410.13842","doi":"10.48550/arxiv.2410.13842","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.13842","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"D-fine: Redefine regression task in detrs as fine-grained distribution refinement","venue":"arXiv (Cornell University)","work_id":"d316d46d-0270-412a-9810-50db1fbe513d","year":2024},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/2410.13842","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:16addb0f224ce6b4032d4135db193e69ecb90aa668b56e031c005cc47fded6af","observation_id":"de73d5f0-477a-4606-8009-93ab890a49cc","resolution":{"observed_at":"2026-05-11T05:20:59.679627Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Vmamba: Visual state space model","venue":null,"work_id":"41348603-9730-411c-8520-1772a4488933","year":2024},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:72a0c95bf1c97e6fa1856651d73921f0d078d97869bbd894191a37c72f4865b5","observation_id":"de4ef93d-cba4-4e01-8fea-7ace0f33945f","resolution":{"observed_at":"2026-05-17T06:39:12.157653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Mamba yolo: A simple baseline for object detection with state space model","venue":null,"work_id":"83e5ea3a-23fb-431a-91d7-0b7cb24658be","year":2025},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:b0078fec379fa9014130e088dd614b72883a115352a483e1188f1cefac5afd24","observation_id":"4e7f42ac-c896-47c1-8299-c1f51dd6fd52","resolution":{"observed_at":"2026-05-17T06:39:12.130802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Ultralytics YOLO","venue":null,"work_id":"0362a204-60db-4a46-874b-0e8748b431c5","year":2023},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:57ace5a1bfdf95dc91f43766e77da4c8c52f0c3ef1537ec7d756e872a8ccf77c","observation_id":"20485d5e-e530-41d1-b22d-a51c474a8029","resolution":{"observed_at":"2026-05-17T06:39:12.074264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2209.02976","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2209.02976","snapshot_observed_at":"2026-07-04T15:49:57.396006Z","title":"Yolov6: A single-stage object detection framework for industrial applications","venue":null,"work_id":"5973a7b7-1bc9-4c39-acc0-8caf1f15138f","year":2022},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/2209.02976","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:5b39d5fd95791ef9abbe8073b8ee8ec592d0c4538ffd087d03c496a514ebd449","observation_id":"66a02764-8bfc-49ad-8d75-0c575809f1e7","resolution":{"observed_at":"2026-05-11T05:20:59.717280Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Hyper-yolo: When visual object detection meets hypergraph computation","venue":null,"work_id":"549f319a-6692-49c1-a4a1-dc0b1e1e56b5","year":2024},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:89c8b05253dbb1a322256c4c5760610fd1cc21638fb06e33e7b80bfb35e511c4","observation_id":"a3ab062d-0e59-4e0b-8630-301e9e5c69ff","resolution":{"observed_at":"2026-05-17T06:39:12.117401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Fbrt-yolo: Faster and better for real- time aerial image detection","venue":null,"work_id":"9bad35bd-06ce-4fa5-8c1a-eb4880b2e03d","year":2025},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:2a68d05dc275283c5026aa372f0a8977a13b2022e7b392644f53a82e96094556","observation_id":"28aadfd1-6fe9-4469-bd49-674cdf1c79b5","resolution":{"observed_at":"2026-05-17T06:39:12.097687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Yolo-ms: Rethinking multi-scale representation learning for real-time object detection","venue":null,"work_id":"ea7f74df-423b-4554-8dc9-97582c984226","year":2025},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:69f7a55d5f94189eccf5d0a61be184846ddf731c0a047b5436609c87b509e6a4","observation_id":"c85cab0a-7a86-4ef9-81e2-31e1d6155eb8","resolution":{"observed_at":"2026-05-17T06:39:12.083269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.03459","last_updated":"2024-06-05T17:07:24Z","snapshot_observed_at":"2026-07-06T18:26:01.717861Z","submitted_at":"2024-06-05T17:07:24Z","title":"LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection","version":1},"cited_work":{"arxiv_id":"2406.03459","doi":"10.48550/arxiv.2406.03459","metadata_source":"arxiv_reference","pith_arxiv_id":"2406.03459","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Lw-detr: A transformer replacement to yolo for real-time detection","venue":"arXiv (Cornell University)","work_id":"6bdf4f58-6cf5-4c7a-9bf8-3329106fde70","year":2024},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/2406.03459","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:e84881470f4f602313ddb5b20fd2ca74bf18108457f7293d00044d5997d87352","observation_id":"20447aa6-2c62-4c87-a878-a3c0a83c8b19","resolution":{"observed_at":"2026-05-11T05:20:59.659841Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Deim: Detr with improved matching for fast convergence","venue":null,"work_id":"9c7ee181-5741-4d90-aeec-8354c68197d0","year":2025},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:4e78a2ec4ba7875e49e8491d91cb43ca20ecc1d227a383606f423ea00aa9f58d","observation_id":"e9239f9b-5bac-400e-a700-857a30a15a87","resolution":{"observed_at":"2026-05-17T06:39:12.077927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks","venue":null,"work_id":"210353d0-50bb-4100-8793-e6f6f1744cde","year":2016},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:481a69e87898e6a2fcbd3a5c6f5a1897ab3a686f0a4884ecf03539489cc7ea61","observation_id":"a8eaa747-4ebd-415f-a8e8-daafb3a721fa","resolution":{"observed_at":"2026-05-17T06:39:12.123766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Mobilemamba: Lightweight multi-receptive visual mamba network","venue":null,"work_id":"db3665ae-983a-4269-b354-2a0d164f2406","year":2025},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:6a5fa06faccfb776acb4933f40b08922911afc0b5456f73c5a7127bdb5f4ee00","observation_id":"ea8a52f3-067b-405d-8950-0e91d8445226","resolution":{"observed_at":"2026-05-17T06:39:12.091375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Focal loss for dense object detection","venue":null,"work_id":"8159489e-1b5c-4603-b37c-e03bd7dda51d","year":2017},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:4a0f4e70cd29d351b378ae94598cb1c9003ab948510d90bd3c8c4e5ae8d8554c","observation_id":"e0cf7c93-5eb6-4666-9693-87190403701e","resolution":{"observed_at":"2026-05-17T06:39:12.094474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"1904.07850","doi":"10.48550/arxiv.1904.07850","metadata_source":"pith","pith_arxiv_id":"1904.07850","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Objects as Points","venue":"cs.CV","work_id":"3567080d-f164-46ab-903a-02853db3a970","year":2019},"citing_paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:54.801912Z"},"links":{"cited_paper":"/paper/1904.07850","citing_paper":"/paper/2604.07759"},"observation_digest":"sha256:21f0f63b7b34ceb1cbc73aad49decc4a3576c1625675695425636428b1d124de","observation_id":"19593340-958c-480f-9eb9-c3b29463ecb5","resolution":{"observed_at":"2026-05-11T05:20:59.724936Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.07759","last_updated":"2026-04-09T03:28:05Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-02T03:50:28.464239Z","submitted_at":"2026-04-09T03:28:05Z","title":"WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":14,"verified_fuzzy":37},"total_outbound_references":51},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2604.07759."}