{"as_of":"2026-08-18T20:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:23231754ce6deccd5f518510667b07a11053cacd3673ae6e3505289f4a2886c7","coverage":[{"denominator":21,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T21:00:03.446500Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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":"2026-08-12T21:00:03.288269Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-12T21:00:03.561322Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"cited_work":{"arxiv_id":"2411.09180","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.09180","snapshot_observed_at":"2026-08-12T21:00:03.561322Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","venue":"cs.CV","work_id":"42c0fb6b-b956-4d60-bad9-db4381c58c4e","year":2024},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.288269Z"},"links":{"cited_paper":"/paper/2411.09180","citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:4779218cad6fe9c3588f25de0345b23f1a123ecf11c781b89a67398f85ce423f","observation_id":"91693539-151d-493e-b67b-5522ceb2bf44","resolution":{"observed_at":"2026-08-12T21:00:03.569843Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.09180/citation-record","integrity":"/paper/2411.09180/integrity","json":"/paper/2411.09180/citation-record.json","paper":"/paper/2411.09180"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"cited_work":{"arxiv_id":"2411.09180","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.09180","snapshot_observed_at":"2026-08-12T21:00:03.561322Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","venue":"cs.CV","work_id":"42c0fb6b-b956-4d60-bad9-db4381c58c4e","year":2024},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.288269Z"},"links":{"cited_paper":"/paper/2411.09180","citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:4779218cad6fe9c3588f25de0345b23f1a123ecf11c781b89a67398f85ce423f","observation_id":"91693539-151d-493e-b67b-5522ceb2bf44","resolution":{"observed_at":"2026-08-12T21:00:03.569843Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:04.041037Z","title":null,"venue":null,"work_id":"7a2ca2e4-3e80-4236-ad04-5314615f50df","year":null},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.297127Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:b67f92b233db76294d488f3e9b46d7145de590afcd2d1161002d5ee5a8a2d1e1","observation_id":"c927f81a-1368-4305-a715-8aa6e27022a8","resolution":{"observed_at":"2026-08-12T21:00:04.047174Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:04.011764Z","title":"Implementation Details The proposed method was evaluated on the VisDrone dataset [14], measuring object detection performance withmAP50, mAP75 and mAP50:95","venue":null,"work_id":"a9ef5206-8cff-49a9-9c31-19220ceaefb6","year":null},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.303421Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:2c4465747ea2c1efa6af658fbf2b9b3ca1dddd15d50f1223236629c3848d917e","observation_id":"39a25d15-f0da-4714-9a3c-d44d372a7069","resolution":{"observed_at":"2026-08-12T21:00:04.018267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.987877Z","title":"First, it removes domain-specific features from the entire scene rather than targeting them at the object level","venue":null,"work_id":"4b3b7933-cf9a-4623-adc8-2c660d97d441","year":null},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.309353Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:ad26a2aff73158df8852a0c92784e66da347e5a54dfc67f9d04b26eeed8cd0d1","observation_id":"2a8a59d3-8d40-45d5-9ef7-a6511187ef11","resolution":{"observed_at":"2026-08-12T21:00:03.996258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.966156Z","title":"Our comparative experiments revealed that LEAP:D outperforms baseline models and other state-of-the-art methods","venue":null,"work_id":"238f05a9-719b-4e4d-a17c-0b8bd814f81e","year":null},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.316381Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:552825e185a97ba5f0538055dffd5d0f819e11a4ea1a8d9e50535144752ae625","observation_id":"253d74e7-9879-4187-a651-5fdc865ce999","resolution":{"observed_at":"2026-08-12T21:00:03.974210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.943958Z","title":"High-resolution processing and sigmoid fusion modules for efficient detection of small objects in an embedded system,","venue":null,"work_id":"1908edda-36f5-4a80-a27b-c55805ea1bc9","year":2023},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.325122Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:c627c3e51daa46f9d1a14d8e7e5034e2b20d325a953608eff4b089e63138776a","observation_id":"c94968eb-dff7-4e14-9729-908e170da914","resolution":{"observed_at":"2026-08-12T21:00:03.950567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.920750Z","title":"Enhanced detection of small objects in aerial imagery: A high-resolution neural network ap- proach with amplified feature pyramid and sigmoid re- weighting,","venue":null,"work_id":"23f54a38-5233-474e-b925-d52b14b160be","year":2024},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.331586Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:7306b6368128ecc35debd2bce35763fffd9060d373103c7b5ac425a15e21dd30","observation_id":"17d15146-b3b5-49eb-83d0-7bec31c64e4f","resolution":{"observed_at":"2026-08-12T21:00:03.928536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.887243Z","title":"Delving into robust object detection from unmanned aerial ve- hicles: A deep nuisance disentanglement approach,","venue":null,"work_id":"a6250b4a-8c2c-4d11-b130-73cac7642165","year":2019},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.338133Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:d2d19220856edd5a3d53d86fd5ec700d8bc91b88bb79710e1f3ec9fb87db85da","observation_id":"8c603a46-df68-477f-863e-2a8c0d489037","resolution":{"observed_at":"2026-08-12T21:00:03.898451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.866056Z","title":"Training domain-invariant object detector faster with feature replay and slow learner,","venue":null,"work_id":"6b70bd1b-e26a-4890-8657-8bcd74c0185e","year":2021},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.345588Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:ac9b264681854859c2d1d83a8374cf40a84f77a6d937f1aa1426359e711f77d3","observation_id":"8ad4fd78-986d-42ff-b8e1-202440e98e60","resolution":{"observed_at":"2026-08-12T21:00:03.872285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.840736Z","title":"ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation,","venue":null,"work_id":"922d871b-e836-4da3-8fb0-eeddb71a3466","year":2022},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.352522Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:fcb28afb1acc45c30c8a81e61adf253e3d8b4cc3f8176b9456831f7272660810","observation_id":"99391b4c-c6b3-41fc-b732-2fee8ff80203","resolution":{"observed_at":"2026-08-12T21:00:03.847105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.818769Z","title":"Domain feature decomposition for efficient object de- tection in aerial images,","venue":null,"work_id":"f0c7b061-219e-452b-bb06-889acefd4a93","year":2024},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.361280Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:e97be4f4a8835c5c24f1e3aaf81c5a088e3ab812888e9f3c4aabdfde5bb07969","observation_id":"09fd50bd-ce96-4c2f-b88d-e9b8c160e230","resolution":{"observed_at":"2026-08-12T21:00:03.825499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.790223Z","title":"Learning to prompt for vision-language models,","venue":null,"work_id":"ee92872a-5e17-4ede-9fa5-7550492f1c3e","year":2022},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.369518Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:1f3d866e567a3d94021879da9b48685f3e296a5725c9e3253a78e011775cbce0","observation_id":"c742609f-f26b-4c96-a0fe-5f9506faf799","resolution":{"observed_at":"2026-08-12T21:00:03.798789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.377805Z","title":"Learning transferable visual models from natural lan- guage supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.377805Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:aa4cd1eed9ccbba6d7980995211d23606d370a4cce873d3a42809fc865adbdb0","observation_id":"f9e150b0-5093-4a57-a334-24b31f27f579","resolution":{"observed_at":"2026-08-12T21:00:03.377805Z","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":"2026-08-12T21:00:03.747768Z","title":"Conditional prompt learning for vision- language models,","venue":null,"work_id":"a5d98597-73ca-4f5c-bc48-47266f1ead42","year":2022},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.390889Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:055c029c3a76147bd345aeb1cfad5ecf800110250b9fbacea361ddb688530fc3","observation_id":"356e448b-c828-4ce6-9f17-de6adc8d7eab","resolution":{"observed_at":"2026-08-12T21:00:03.754765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.725860Z","title":"Clip the gap: A single domain generalization approach for object detection,","venue":null,"work_id":"0d89a820-5fa7-4625-af0d-90457b188a98","year":2023},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.396900Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:9ddc4b78eaa55300f8e354ed2e586621d180e67ade23137f09b4a53c24d966bc","observation_id":"bf44fcbd-21ff-4ebb-ae0f-500e02d69ae2","resolution":{"observed_at":"2026-08-12T21:00:03.733579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.700253Z","title":"Shooting condition insensitive unmanned aerial vehicle object detection,","venue":null,"work_id":"e8c620be-7f7b-43b6-8f02-bb0a5ec66138","year":2024},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.403558Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:f26c78eff9c9809f4a6dafd0bc9e5c6a4efb584f0cd7c15a1f44ec472d7f92dc","observation_id":"194fb227-9c09-43fb-a674-eb84b47de77b","resolution":{"observed_at":"2026-08-12T21:00:03.707966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.676853Z","title":"Fast r-cnn,","venue":null,"work_id":"3fecb0ea-21b1-4313-a48e-2d9ad84d1a9a","year":2015},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.409986Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:6ce8615a38a9ffbab7c819db6ed73a4c69bc6612cbc16c1960a7162f16cdfc4f","observation_id":"a3d8ceb7-c164-44bc-8a5e-008830364a3e","resolution":{"observed_at":"2026-08-12T21:00:03.684910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.641987Z","title":"Feature pyra- mid networks for object detection,","venue":null,"work_id":"a683576a-d33d-40ae-9ad6-bec0c19528f8","year":2017},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.418883Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:5344ec292e95fc0eb98f44131e1517c6a45d8a8d92d08f01c3a6a154144f3da7","observation_id":"b9a5f3ba-cc11-4157-8430-7779f01e484e","resolution":{"observed_at":"2026-08-12T21:00:03.652414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-12T21:00:03.611612Z","title":"Detection and tracking meet drones challenge,","venue":null,"work_id":"a53ddd9b-576a-4e0a-91dc-dc1dcd7d0f33","year":2021},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.427314Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:6b244aa55a6bf6e4c022c8b03702bce5a85e1e7c210b564e6d679282ae87d3a5","observation_id":"c9a900f0-b4e7-462a-882c-9ba71165d82d","resolution":{"observed_at":"2026-08-12T21:00:03.620444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.04747","last_updated":"2017-06-15T13:21:04Z","snapshot_observed_at":"2026-08-15T03:49:17.013617Z","submitted_at":"2016-09-15T17:32:34Z","title":"An overview of gradient descent optimization algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.04747","snapshot_observed_at":"2026-08-12T21:00:03.437335Z","title":"An overview of gradient descent opti- mization algorithms,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.437335Z"},"links":{"cited_paper":"/paper/1609.04747","citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:e66ba193e04b44db06976e4e1e586c029dc6e6e9b171b00f610fce6b51ba8f4e","observation_id":"f9961ec9-56b6-46ac-bf32-b6eecab16a10","resolution":{"observed_at":"2026-08-12T21:00:03.437335Z","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":"2026-08-12T21:00:03.587392Z","title":"Cascade r-cnn: Delving into high quality object detection,","venue":null,"work_id":"1d1a2b09-3e20-4258-a856-0b625b9bd8fd","year":2018},"citing_paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T21:00:03.446500Z"},"links":{"citing_paper":"/paper/2411.09180"},"observation_digest":"sha256:daa8395b39909b4fc2af52a29d52630ebdd15f824bfe400031139e0aea3c6556","observation_id":"f17e7c64-4944-4549-9eab-5e1d4eafb59d","resolution":{"observed_at":"2026-08-12T21:00:03.595526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.09180","last_updated":"2024-11-14T04:39:10Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-18T16:12:06.227201Z","submitted_at":"2024-11-14T04:39:10Z","title":"LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection"},"reference_resolution":{"displayed":21,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":17},"total_outbound_references":21},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 1 inbound Pith citation observation for arXiv:2411.09180."}