{"as_of":"2026-08-11T14:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:68c10a39e27c969049a160ca7da5bf8b6b4ffa334818c3cdf1420b0387f0da33","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:15:44.848524Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.22161/citation-record","integrity":"/paper/2506.22161/integrity","json":"/paper/2506.22161/citation-record.json","paper":"/paper/2506.22161"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:50.991014Z","title":"Yolo9000: better, faster, stronger,","venue":null,"work_id":"117b46d8-48b7-4f59-bdb4-f12b05567e81","year":2017},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:40.606044Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:e5d3deb0eb944976aa0e1c1cd8aab67327db256bfe61a061fb5423362f8d11d1","observation_id":"fa88f2d3-248a-4889-8250-6fb69fea2be7","resolution":{"observed_at":"2026-08-06T22:15:51.047794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:50.879183Z","title":"Crossdet++: Growing crossline representation for object detection,","venue":null,"work_id":"55c7ecc3-406a-43fa-901f-6ba99e1c0338","year":2022},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:40.714198Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:330aec87f737269204a23cdff71b9a5036743fc5777c2434943409bc72045518","observation_id":"429bcfd2-9c47-4630-a793-e0a53a9851b3","resolution":{"observed_at":"2026-08-06T22:15:50.934425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:50.763577Z","title":"Fast R-CNN,","venue":null,"work_id":"0d246767-f6d0-471c-bb5a-eb0b4eb5435d","year":2015},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:40.912361Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:b281b0f484f4352061a3f5f6524869ccdad44757fef5da012828f05d35bf15d8","observation_id":"ad469d54-b326-47aa-899e-1f7a66209672","resolution":{"observed_at":"2026-08-06T22:15:50.816124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:50.650816Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks,","venue":null,"work_id":"692ce3c6-2eeb-4322-aea3-8db21b30df2e","year":2016},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:40.999039Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:0c1dd39262a2ce442a0b8991541c1ee55e205dac2fd88de59a7555733ab14427","observation_id":"f8bb1d20-1202-419f-b78e-245361de86a1","resolution":{"observed_at":"2026-08-06T22:15:50.698423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:50.541579Z","title":"Cascade r-cnn: Delving into high quality object detection,","venue":null,"work_id":"29531509-9dcc-489f-863c-ccc0be271972","year":2018},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:41.143972Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:01d5911b83acb447367238efee8a4091730d65bfc9aa4943bcb18f079badc022","observation_id":"2a2d26c6-da3c-4909-9531-30ad539a81e1","resolution":{"observed_at":"2026-08-06T22:15:50.593370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:50.396111Z","title":"Frustratingly simple few-shot object detection,","venue":null,"work_id":"463a637a-be0a-4d76-ab53-28382750e51b","year":2020},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:41.287311Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:608dccb355f4c67d2e4234c0dd5c388372e935acde54d9f7a4e21c0cef62f981","observation_id":"3d527f15-0605-44cd-b95c-1c23e3395cf1","resolution":{"observed_at":"2026-08-06T22:15:50.468061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:50.278648Z","title":"Defrcn: Decoupled faster r-cnn for few-shot object detection,","venue":null,"work_id":"c1078844-dbaa-4cad-88e9-9e79c16917d3","year":2021},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:41.435716Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:8c86add0165bc6db1c58f68887528656a2a3c59132288105c5f342da2b378f53","observation_id":"45b6cac6-eed3-46dd-9963-75aa7aaf000b","resolution":{"observed_at":"2026-08-06T22:15:50.328199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:50.141598Z","title":"Few-shot object detection via association and discrimination,","venue":null,"work_id":"6875163c-e28c-43f3-bfd3-3b6c035aa742","year":2021},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:41.511204Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:b2b8526c416c798dd4581448d9b1aa57213aeff49932a635424f0ff289ce72b7","observation_id":"11ef8339-fc94-4a06-93f7-451ff15f68ef","resolution":{"observed_at":"2026-08-06T22:15:50.204832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:49.964753Z","title":"Label, verify, correct: A simple few shot object detection method,","venue":null,"work_id":"03c53ab3-5126-4f1f-97df-d77182b32460","year":2022},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:41.613113Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:04d044944e947f0bf95e3fabcaa451ee3d3cdc29a802cc340481adc66bf58812","observation_id":"a08e215e-f56d-4e67-9ece-9d70d8fe76b0","resolution":{"observed_at":"2026-08-06T22:15:50.034444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:49.798112Z","title":"Semi-supervised few-shot object detection via adaptive pseudo labeling,","venue":null,"work_id":"91b1dcda-9ea5-4340-8f05-5f684a8dbe94","year":2023},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:41.701138Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:b6d70d4e49f14742b0b547efd039e493a5c7b36af22d43aae94ce7164dacdf7b","observation_id":"3538393c-8879-43d9-84e4-61fe97c960b7","resolution":{"observed_at":"2026-08-06T22:15:49.869534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:49.711388Z","title":"Vlm-guided explicit-implicit complementary novel class se- mantic learning for few-shot object detection,","venue":null,"work_id":"cf45aa68-c01f-4093-92bb-b9c18261cde4","year":2024},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:41.802119Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:0408371c6101b080d5a94b2a404c61ffc48c9eeb4ff169cc99fd6040d6cd24ee","observation_id":"3a6db5c3-a940-4e13-877a-3b555e423585","resolution":{"observed_at":"2026-08-06T22:15:49.743236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:41.889673Z","title":"Ecea: Extensible co-existing attention for few-shot object detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:41.889673Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:86338b57860152a412c2e01babc84304a845da6980eb77bf5d3546c18a7c9327","observation_id":"582fe357-4329-4374-8b7d-049235c57d8c","resolution":{"observed_at":"2026-08-06T22:15:41.889673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:49.588284Z","title":"Exploring orthogonality in open world object detection,","venue":null,"work_id":"70edbe7a-73f9-44af-8fed-d484a37b84ac","year":2024},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:41.950727Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:7f21a24e3829b10bd6228310603b5d2b453b7fb1b336d5101ff8e25b5531f6e1","observation_id":"844d7935-5299-4f86-a3bb-34b183be2244","resolution":{"observed_at":"2026-08-06T22:15:49.654376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:49.482211Z","title":"Norm-aware embedding for efficient person search,","venue":null,"work_id":"3d1a5015-600b-4a21-b4e5-3d3af6eb3a72","year":2020},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:42.022387Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:750bbc455fc9f1c69f3f72394412399b5461b2abe4a9b6e9a6109fad318a17d1","observation_id":"077d2a0e-5310-4a04-ad6d-cb832dbe7659","resolution":{"observed_at":"2026-08-06T22:15:49.537153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:49.388787Z","title":"Few-shot object detection via feature reweighting,","venue":null,"work_id":"4504a118-7c7c-401a-876b-c51058b51588","year":2019},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:42.130601Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:1f58aab97c15b9d7dc144939c34c77402fb5a4e8c85f98c106c6bb9bd1c4590c","observation_id":"366e477c-7a45-4f8f-b736-62bf6fd01002","resolution":{"observed_at":"2026-08-06T22:15:49.429957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:49.289338Z","title":"Beyond max-margin: Class margin equilibrium for few-shot object detection,","venue":null,"work_id":"af3c4b7c-7b58-4960-8705-686200d79e15","year":2021},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:42.202483Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:f527f2855e8889e5c5c5048f093b774dd8c29dacf11aad04ebacda2a4bf0f3e1","observation_id":"a68381b3-8344-4173-b248-bec65e1319a8","resolution":{"observed_at":"2026-08-06T22:15:49.335332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:42.271576Z","title":"Few-shot object detection: Research advances and challenges,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:42.271576Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:f5ba6fbc828caa3524064483452167c500e76b64876381328e918fbb1fbcc328","observation_id":"65c52bad-7192-4fd9-a174-6ea9b8aa7ce5","resolution":{"observed_at":"2026-08-06T22:15:42.271576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:49.187654Z","title":"Adaptive subspaces for few-shot learning,","venue":null,"work_id":"7a837835-b84d-47ea-95a8-3ad25a443a7c","year":2020},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:42.363403Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:a7d13dc45e046c40a6b3a066d777c09f11ea207d11de0ec2618520138b766853","observation_id":"4f117369-0f69-491c-8c44-bcdebcf4f18c","resolution":{"observed_at":"2026-08-06T22:15:49.248809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:49.102976Z","title":"Constrained few-shot class-incremental learning,","venue":null,"work_id":"9924e382-7818-42cb-80fc-ddf101b8acbe","year":2022},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:42.446844Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:f11c52398c0528d3be1d2735ef7ff6233b23eecaca9122cd55e4b7ad89def9fe","observation_id":"c8721a1c-dd8c-4ed2-ac0d-d887c546178d","resolution":{"observed_at":"2026-08-06T22:15:49.139539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:49.005951Z","title":"Orthogonal projection loss,","venue":null,"work_id":"a1700d10-32de-4737-b8bc-45bb02a30f25","year":2021},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:42.548524Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:e96eba84f6213659dda522fffd002cc1b455aa53fc91dd3f83985e42ebc6c275","observation_id":"15da09d2-2904-4104-b498-7444c7d95ac1","resolution":{"observed_at":"2026-08-06T22:15:49.049640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:48.903559Z","title":"Learning orthogonal prototypes for generalized few-shot semantic segmentation,","venue":null,"work_id":"d2903d7a-eb15-4b57-b18e-599159ff5612","year":2023},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:42.623463Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:0fbc6c3f00dd966846a87959f3634c1480c990aa79cd07a23177ac741a12ab6c","observation_id":"837e51d8-1c25-4a44-b9cd-5209113f9e44","resolution":{"observed_at":"2026-08-06T22:15:48.956016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:48.791020Z","title":"Generating features with increased crop- related diversity for few-shot object detection,","venue":null,"work_id":"d7f71d4a-c8b4-4af9-b898-a41b4e577159","year":2023},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:42.710555Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:6d6da0a0891427e3498c48318d1df12ee44e575f62bedf023b35d7c5db0279af","observation_id":"ddd34083-de4f-4066-a23f-824e9f81f6f6","resolution":{"observed_at":"2026-08-06T22:15:48.837969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:48.592663Z","title":"Simple copy-paste is a strong data augmentation method for instance segmentation,","venue":null,"work_id":"9901a494-e2b5-44e4-8799-6e14416dab15","year":2021},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:42.793715Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:cd26932fbfd871f6f7d1e30ea166ef132bbc2ac744e74fef33d55183fec6a5e2","observation_id":"a61c8d8b-46e4-4fe1-b3ba-98c3d0aea1e6","resolution":{"observed_at":"2026-08-06T22:15:48.671956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:48.442069Z","title":"X-paste: Revisiting scalable copy-paste for instance segmentation using clip and stablediffusion,","venue":null,"work_id":"9a77df4c-6fe2-49db-bc12-79576ec8cf04","year":2023},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:42.882630Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:0d579f43c90377133b2a7e62e74ce0a4b51bb52f65175aee5677002b816e3604","observation_id":"a72b404e-6128-4af3-8733-8d195d2e092f","resolution":{"observed_at":"2026-08-06T22:15:48.512100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:48.322571Z","title":"Pin: Posi- tional insert unlocks object localisation abilities in vlms,","venue":null,"work_id":"451823b2-2246-425f-bf9a-789cabe5f80a","year":2024},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:42.985177Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:5949f4ccdadfa015a4d96bdeaa37295d206157797762a3a5cd2d41d4cdc570cc","observation_id":"2f0d6aa3-3073-45f2-a5d3-e3f793d1e8be","resolution":{"observed_at":"2026-08-06T22:15:48.369259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:48.174222Z","title":"An effective crop-paste pipeline for few-shot object detection,","venue":null,"work_id":"6282157a-c650-428b-8dc9-ce25ffbb3b2b","year":2023},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:43.114599Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:7907004f301b15199348cb591448f3879028ba14851d6d1e4065e36748ade397","observation_id":"2d6e18ec-8063-48df-ac95-4334be5a5679","resolution":{"observed_at":"2026-08-06T22:15:48.237009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:48.035706Z","title":"Learning to detect every thing in an open world,","venue":null,"work_id":"8d9347d2-939d-40e1-bdc9-f66833c5f02c","year":2022},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:43.202687Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:6b70ed4220a004190eadb5c680400ec64ab1bd31fdc582308a0963b69d63f48e","observation_id":"7f06990f-d15f-4791-a0e8-7ed206ca925a","resolution":{"observed_at":"2026-08-06T22:15:48.108148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:47.853478Z","title":"Fsce: Few-shot object detection via contrastive proposal encoding,","venue":null,"work_id":"f9fd6d77-34cf-4c49-915d-e7057ba9acb6","year":2021},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:43.284323Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:b10223009f24a45554455d1247afcc6fd1180e9b911303fe351140b37f2cf8aa","observation_id":"d29a1e11-61b5-43a0-928b-26f2ce6e7b85","resolution":{"observed_at":"2026-08-06T22:15:47.937853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:47.688731Z","title":"Orthogonal progressive network for few-shot object detection,","venue":null,"work_id":"c5ebe09d-75c3-4b06-ae2d-6fddd6b197d3","year":2025},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:43.377262Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:3d176084c1a91a1340be0045f4a53cf593c2ca7fedd4d6a96c5009b03ca43d4a","observation_id":"8b26d9cb-b89b-4e93-b2b8-193a7f898b8c","resolution":{"observed_at":"2026-08-06T22:15:47.743576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:47.535810Z","title":"Cat: Localization and identification cascade detection transformer for open- world object detection,","venue":null,"work_id":"52b85c92-0f8d-4f73-aa45-cca47a8a55ba","year":2023},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:43.447058Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:86c8fc6fb8bf2c557e46899926e60b91a355551743fb0d0cb2871508d3ea4ec1","observation_id":"543b95f7-f193-49cf-8402-3f7e5e6b97c7","resolution":{"observed_at":"2026-08-06T22:15:47.611221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00714","last_updated":"2024-10-28T16:37:57Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T17:00:08Z","title":"SAM 2: Segment Anything in Images and Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00714","snapshot_observed_at":"2026-08-06T22:15:43.531497Z","title":"Sam 2: Segment anything in images and videos,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:43.531497Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:31601b9c424c877cac8828664cf6b788633c0840af3991c32e5a440423eecf7a","observation_id":"47f41dfb-7a21-400b-96e2-173383e67ed6","resolution":{"observed_at":"2026-08-06T22:15:43.531497Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:47.380973Z","title":"Bridging composite and real: towards end-to-end deep image matting,","venue":null,"work_id":"a1cf0f69-884f-46f5-ac6d-3d5fb1dab8b2","year":2022},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:43.606576Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:db375abd5f4f64c091c404f0e543939265da0036db4847cce07c2de97824989d","observation_id":"c5aade23-beb5-41e7-84d4-7aa2bbbdf9ae","resolution":{"observed_at":"2026-08-06T22:15:47.466394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:47.213686Z","title":"Multi-faceted distillation of base-novel commonality for few-shot object detection,","venue":null,"work_id":"2a7a064a-e07f-47fc-9609-7baf47e93e4c","year":2022},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:43.682515Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:0743e17d29d43bf99d7667146bf131542f191a898b9218c99e13dfd19c65c808","observation_id":"254a16d9-e1f7-4f93-9b04-2d47dd943ec2","resolution":{"observed_at":"2026-08-06T22:15:47.303448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:47.007589Z","title":"Few-shot object detection via variational feature aggregation,","venue":null,"work_id":"c40cede5-9ada-40f1-9ad2-a7b8831b9bb5","year":2023},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:43.770920Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:843404f096fb03532258f74c3199421932535df4c6f577dbff5391bd5c50aa35","observation_id":"55d3cde8-4c48-462d-95f6-2bb72c43858f","resolution":{"observed_at":"2026-08-06T22:15:47.110777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:46.817631Z","title":"Mcce-rec: Mllm- driven cross-modal contrastive entropy model for zero-shot referring expression comprehension,","venue":null,"work_id":"22f294df-2002-446d-9c53-a27e0210b557","year":2024},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:43.866422Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:928a6d2ba9f9374b6d6c29376479031ccf91a00b294e3632cea4985ca5e624fe","observation_id":"b360cd37-c9f0-4541-ac0b-d547d3d641c0","resolution":{"observed_at":"2026-08-06T22:15:46.906009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:46.676896Z","title":"Meta-detr: Image- level few-shot detection with inter-class correlation exploitation,","venue":null,"work_id":"96a357fb-385a-4f40-a36d-9ffb580cb759","year":2022},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:43.961090Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:243b6a09201b67ac17fd1606a40b5c7a72643fbd50b83928ef81b4ac43c1a85c","observation_id":"1ffc2318-9276-4a2e-8e79-deeb4e3f8aa8","resolution":{"observed_at":"2026-08-06T22:15:46.743218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:46.502790Z","title":"Fine-grained prototypes distillation for few-shot object detection,","venue":null,"work_id":"03a589ab-24cb-4046-8824-5af69fa10a99","year":2024},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:44.060160Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:5198c2952aa617e344ef74c26cffb9911a4a2708f0af6ef38110bacdcc8fc369","observation_id":"3e7b51d3-2e3c-4809-a45e-29b1b58e1b06","resolution":{"observed_at":"2026-08-06T22:15:46.579274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:46.333035Z","title":"Understanding negative proposals in generic few-shot object detection,","venue":null,"work_id":"2fc368a7-8e2d-4c36-bb5d-43ea16538468","year":2024},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:44.137698Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:5ade42a1f6540945854326756d8da5a90e1f01896fe6998c43ec85180bc46d2c","observation_id":"62f32172-6cc8-4369-b4c3-a25f8cd469d3","resolution":{"observed_at":"2026-08-06T22:15:46.414229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:46.198075Z","title":"Fsna: Few-shot object detection via neighborhood information adaption and all attention,","venue":null,"work_id":"2733281b-0d94-497d-ad16-14d1877fbc1c","year":2024},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:44.221637Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:bf2a9c23cf32563f920fea945aeca8288fc942cca3a679adac770069110be827","observation_id":"54ea160d-9c61-4283-9c0f-770d60ae9a21","resolution":{"observed_at":"2026-08-06T22:15:46.250578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:45.983615Z","title":"Multi-view part-based few-shot object detection,","venue":null,"work_id":"1f2a1286-8fa0-413b-889a-ca78a1eb50b4","year":2024},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:44.314856Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:661b1eb70d3f767006dc412519de3358449d8f2ac838aa98bf1e64ed7d18be7a","observation_id":"4e8d9824-44e0-439d-b55d-493ad1524227","resolution":{"observed_at":"2026-08-06T22:15:46.079169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:45.749097Z","title":"Text generation and multi-modal knowledge transfer for few- shot object detection,","venue":null,"work_id":"74feba5c-2fe0-4f35-a398-4baaa8ff7b9e","year":2025},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:44.387873Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:a62f11bf01191f90177df8b923291410e74351d8d1e9c28d45261813fae18402","observation_id":"ff1bbb8e-93f7-4744-a686-89606e976c04","resolution":{"observed_at":"2026-08-06T22:15:45.851352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:45.527991Z","title":"The pascal visual object classes (voc) challenge,","venue":null,"work_id":"8991396f-f25e-4333-a3c0-d1a825d63f40","year":2010},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:44.483478Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:f7d7c59adf14e284c36025a81539c316341ac2615ef2f693cc439741b93fa59b","observation_id":"dcac011f-19b6-4a52-9fb4-542107c82f45","resolution":{"observed_at":"2026-08-06T22:15:45.633307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:45.317000Z","title":"Microsoft coco: Common objects in context,","venue":null,"work_id":"c89f5ab5-0ac4-43dd-9296-70b0a2cb8f07","year":2014},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:44.564470Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:655303be1e3feb66ba801fba844a5277da60315d65d49e6f09be639ff659e62b","observation_id":"c6c1438a-cba9-46db-a542-daf5d453a638","resolution":{"observed_at":"2026-08-06T22:15:45.422904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:45.139387Z","title":"Multi-scale positive sample re- finement for few-shot object detection,","venue":null,"work_id":"2e4504b7-8b16-4385-af0f-6463724189a8","year":2020},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:44.664434Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:36d9427ca390f0bca1bc39281e94caf6ea2a05c9ccc5220d646021a683cb7f35","observation_id":"b82f4b96-c682-4788-923e-c06bede91142","resolution":{"observed_at":"2026-08-06T22:15:45.233735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:44.966139Z","title":"Disentangle and remerge: interventional knowledge distillation for few-shot object detection from a conditional causal perspective,","venue":null,"work_id":"60adb4f2-1953-4479-b5ad-98360320da0c","year":2023},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:44.754394Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:6c07e58ec33b5d2d6f815989ef0097d3a5ac74190e0793b69b7ca6a791240396","observation_id":"7d6653fa-3f7d-4520-9437-76f0b6af6a43","resolution":{"observed_at":"2026-08-06T22:15:45.051074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:15:44.848524Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T22:15:44.848524Z"},"links":{"citing_paper":"/paper/2506.22161"},"observation_digest":"sha256:e3f57b8bf653d8de7452b082bd81a45593763ab097ba02c80539bf38f15cf870","observation_id":"ac9515f2-4d25-48f8-bd32-c1da8c1c11b9","resolution":{"observed_at":"2026-08-06T22:15:44.848524Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.22161","last_updated":"2025-06-27T12:17:04Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T22:07:24.901789Z","submitted_at":"2025-06-27T12:17:04Z","title":"Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":0,"verified_fuzzy":42},"total_outbound_references":46},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2506.22161."}