{"as_of":"2026-08-20T13:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b5876f592be7ab6b7d290723c8192e0f23d82ee94875cae9b81e820eedcaed2b","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-09T17:59:03.728392Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2607.07192/citation-record","integrity":"/paper/2607.07192/integrity","json":"/paper/2607.07192/citation-record.json","paper":"/paper/2607.07192"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T18:06:26.347691Z","title":"Clip the gap: A single domain generalization approach for object detection,","venue":null,"work_id":"ec9a5ca4-1b05-402c-a161-f8acb343ed38","year":2023},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:ce8ad6faf9f1b1ad5dfacdf93dd7d5477100314443d4b6cfc0ffc2cf11b46c9e","observation_id":"0ef51ebc-bbbe-412b-8d08-9b303b354b19","resolution":{"observed_at":"2026-07-09T18:06:26.348842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.326594Z","title":"Improving single domain-generalized object detection: A focus on diversification and alignment","venue":null,"work_id":"48be02a3-75b5-425b-a95a-0c3981bf2452","year":2024},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:deb2b108d9c2a22e736f7dc79717c90d660b32b9122a645eccdab0a5ec7c832c","observation_id":"54f1f4ac-ceac-4ad6-bd83-f9cf952b1e9a","resolution":{"observed_at":"2026-07-09T18:06:26.327823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.314218Z","title":"Unbiased faster r-cnn for single-source domain generalized object detection,","venue":null,"work_id":"781ac6be-c580-4e1c-91cc-81685efdbc35","year":2024},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:52c2fbe19af0add71132f800a0ac9e4d85bca13dce4d87c0776dd412604c1498","observation_id":"ac078720-3b54-4c56-9d7c-66d93ea9956f","resolution":{"observed_at":"2026-07-09T18:06:26.315451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.340874Z","title":"Behind every domain there is a shift: Adapting distortion-aware vision transformers for panoramic semantic segmentation,","venue":null,"work_id":"f5f86a77-58d7-4645-8107-5a47f338e819","year":2024},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:8f44f5f02ff6f276f05c6616f453903679abcaa3de96aeb3f11fc838c07eca03","observation_id":"27ce7d44-0ac9-4f0b-9e3d-b223aa76fc4d","resolution":{"observed_at":"2026-07-09T18:06:26.342078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.344279Z","title":"Tib: Detecting unknown objects via two-stream information bottleneck,","venue":null,"work_id":"bcdea7ea-53d1-401b-b423-380a80bc393e","year":2023},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:9180967495c7cf525bf93b5e0b009ff9c9096b22499e651d0adde596daea0e7b","observation_id":"f934ac89-ae50-47b4-862a-b3744ab9e302","resolution":{"observed_at":"2026-07-09T18:06:26.345471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.349384Z","title":"Vector-decomposed disentanglement for domain-invariant object detection,","venue":null,"work_id":"ab116f63-49b9-4213-a894-668b720ff1b0","year":2021},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:c3c29895c722dbd41a6eb634badd6b66781c5f3da30c6d2a60ab4fdc7a42e9d6","observation_id":"ae7d8fc6-9a48-4314-9847-9add00b4cae4","resolution":{"observed_at":"2026-07-09T18:06:26.350554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.339043Z","title":"Universal-prototype enhancing for few-shot object detection,","venue":null,"work_id":"6853cb95-2c05-4d67-9de2-effeca57ed27","year":2021},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:06ce854e592c04410738e08644fb3fbe2c562955420fa9f8f0e1ae4c249b42f2","observation_id":"c1f4c995-be2d-4df4-98ec-534952f857dd","resolution":{"observed_at":"2026-07-09T18:06:26.340184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.334024Z","title":"Poda: Prompt-driven zero-shot domain adaptation,","venue":null,"work_id":"5ec67b91-e71e-43fb-a327-1b35c722d60a","year":2023},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:55f029c2cd85e0ab20f812569c0ee8e4fb967ec94368acc213b54957f70f067e","observation_id":"711627ae-ebea-4351-9c2f-5eb4d1fd30ab","resolution":{"observed_at":"2026-07-09T18:06:26.335238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.335750Z","title":"Style evolving along chain-of-thought for unknown-domain object detection,","venue":null,"work_id":"13dea08f-4d5c-4e1a-923c-95eee6defb0a","year":2025},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:ba1ada975ba2e34017eed14863c735de551381b990aba06269526b34e9613e1b","observation_id":"a44cac7e-3340-4e28-ba11-225eb5cba292","resolution":{"observed_at":"2026-07-09T18:06:26.336934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.337405Z","title":"Prompt-driven dynamic object- centric learning for single domain generalization,","venue":null,"work_id":"30d95237-75eb-4e9b-80fd-c5cbe5e5f82c","year":2024},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:fa526afa338a60e4eadd80379c37271f9e1c349cff7134a4d6fafb3825058b72","observation_id":"d330510d-565e-4bd4-8b84-756345b6d4de","resolution":{"observed_at":"2026-07-09T18:06:26.338529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.345993Z","title":"Percept, memory, and imagine: World feature sim- ulating for open-domain unknown object detection,","venue":null,"work_id":"5e4987cb-5b43-43d8-b8d0-53974f295b2a","year":2025},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:905e69b13e7a221c9870c67e5e8cfaf3863398a55ac7fcb5a9f5431304baa959","observation_id":"e843501b-9e0c-4ca4-9434-18c453504b52","resolution":{"observed_at":"2026-07-09T18:06:26.347104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.328323Z","title":"Source-free domain adaptation with frozen multimodal foundation model","venue":null,"work_id":"565176ce-6e5e-4803-b48f-e38b4636c6a3","year":2024},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:4a67ccf88e3a9a517ec7c2a95d4ef2386122afdd3b85c382df0f761be4ad96b3","observation_id":"11249b0e-a493-4be7-a289-68303d708b13","resolution":{"observed_at":"2026-07-09T18:06:26.329473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.323550Z","title":"Towards ood object detection with unknown- concept guided feature diffusion,","venue":null,"work_id":"39eb786d-8232-43e3-a87c-c4c61514917f","year":2025},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:deaea4ab840ce284c12ba38479c93e994676766f80c2e482d7c035948138533c","observation_id":"713ab19a-79d4-4993-8705-6267a1112273","resolution":{"observed_at":"2026-07-09T18:06:26.324725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2511.13720","last_updated":"2026-01-07T05:36:57Z","snapshot_observed_at":"2026-08-18T02:11:41.876479Z","submitted_at":"2025-11-17T18:59:57Z","title":"Back to Basics: Let Denoising Generative Models Denoise","version":2},"cited_work":{"arxiv_id":"2511.13720","doi":"10.48550/arxiv.2511.13720","metadata_source":"pith","pith_arxiv_id":"2511.13720","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Back to Basics: Let Denoising Generative Models Denoise","venue":"cs.CV","work_id":"37973de8-a5e6-4d92-897b-a98fa9f7f2f3","year":2025},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"cited_paper":"/paper/2511.13720","citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:068c0203ce6a596fc635c9f26610d837ab5b6271b18ca61cdb32377af9c88f9e","observation_id":"ae5f546c-c445-4712-a66c-55d226c004e8","resolution":{"observed_at":"2026-07-09T18:06:25.907765Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-17T16:38:11.792909+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-17T16:38:11.792909+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.329988Z","title":"Deep feature deblurring diffusion for detecting out-of-distribution objects,","venue":null,"work_id":"bebffdd4-872f-46a5-ba72-c5eba34b517f","year":2023},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:f5e71e6592388e9cc2624c5a0123968f74b5e14d7651ab7e75998ceb801f9b9f","observation_id":"724de79a-0557-4b20-8a81-99b149f3090d","resolution":{"observed_at":"2026-07-09T18:06:26.331199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.341949Z","title":"Single-domain generalized object detection in urban scene via cyclic-disentangled self-distillation,","venue":null,"work_id":"901bb1a5-9d74-4858-ab99-e04ad59d38fc","year":2022},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:6068a7b7091fdb8005e7cbea5f3c03fbec5c2e919d39ab4d17ce35dc7d2c8031","observation_id":"334b2d3a-9f3d-4d9c-8218-0ec05a35d6c8","resolution":{"observed_at":"2026-07-09T18:06:26.343230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.325223Z","title":"Robust domain adaptive object detection with unified multi-granularity alignment,","venue":null,"work_id":"84a1bbb9-1ee7-4fd6-8d68-21c5cb6a05c7","year":2024},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:bf08198cbc3278414dc1a3b6096869c080e41bd0039c9c1478253e10830d5edc","observation_id":"59713230-78af-45a6-bce2-78fab5eca6bf","resolution":{"observed_at":"2026-07-09T18:06:26.326352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.328598Z","title":"Single-domain generalized object detection with frequency whitening and contrastive learning,","venue":null,"work_id":"3006aeec-7736-42a5-97a9-6b217fa8b334","year":2025},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:ac9c704afaf9903150312ca1427bc2a5759672db6c7c57b05994f3fb3d0931aa","observation_id":"d0b1b073-acf0-497b-a3b9-6b1ba8d7f0ed","resolution":{"observed_at":"2026-07-09T18:06:26.329809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.330288Z","title":"A comprehensive survey on source-free domain adaptation,","venue":null,"work_id":"75d0fa3f-8b7c-4b1e-aaf7-a90d77046b55","year":2024},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:6d9d07d3611af0e556e87ab4b9f4ada8d34c2a933ea9eae5403498998278ee1f","observation_id":"2a9d1862-c74e-4266-b44e-51ec8357fcbc","resolution":{"observed_at":"2026-07-09T18:06:26.331819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.332291Z","title":"G-nas: Generalizable neural architecture search for single domain generalization object detection","venue":null,"work_id":"ecb05868-8f89-44e4-95e8-b0e0f12c51f2","year":2024},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:85d25e5674239abaa54c53d8f1459a3399f28601ab349815733383d8dcbcd6e0","observation_id":"9b1cfcd1-90c9-499a-bf6b-3ed9b7178814","resolution":{"observed_at":"2026-07-09T18:06:26.333539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.342596Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":"f6fa43d9-b6b3-4248-90e3-ab8a8a0ba2b2","year":2021},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:d00bd8e572c499177d1a41b1f90d05836313fb894d5c81b4f145bd23e9b8a829","observation_id":"7bcbce26-538e-4fc7-b222-7bc2951c2e43","resolution":{"observed_at":"2026-07-09T18:06:26.343729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.324968Z","title":"Deep defocus map estimation using domain adaptation,","venue":null,"work_id":"e40f2fab-0bcc-493e-af32-195137f2d758","year":2019},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:e3480993d870dc197571552d66e5863f4a3dbe490f8ebcbaebcf42aba3b59967","observation_id":"23546d76-fa12-48db-860f-bb248d143d7f","resolution":{"observed_at":"2026-07-09T18:06:26.326113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.340393Z","title":"Geodesic regression and the theory of least squares on riemannian manifolds,","venue":null,"work_id":"5f96b669-625a-4c0e-a484-7adcb25a3bfb","year":2013},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:4f2834fbeef0588fce03b4b41e923c742c1fa444cd919b66816865ad7f096b4d","observation_id":"204d8391-130e-4045-b719-031a05d61a28","resolution":{"observed_at":"2026-07-09T18:06:26.341477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.314493Z","title":"Contractive auto-encoders: Explicit invariance during feature extraction,","venue":null,"work_id":"8c369abc-aed6-4330-8474-ed46dc059bb2","year":2011},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:f0c9bb8ad3a708db7c07979b086ce122434837353149420322fa2497af39b0a2","observation_id":"93889411-ef59-4ceb-b572-c4bebadb8923","resolution":{"observed_at":"2026-07-09T18:06:26.315611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.318216Z","title":"A connection between score matching and denoising au- toencoders,","venue":null,"work_id":"22084db3-f1ae-4fe1-8b43-e667694b8e44","year":2011},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:911bacf6c6f6338454a71dacc6d489dc4219c5f044178d5950616665b6538d99","observation_id":"f4e9b3e9-749d-4905-b2c8-d120af74bf06","resolution":{"observed_at":"2026-07-09T18:06:26.319391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.303569Z","title":"Show, attend and tell: Neural image caption generation with visual attention,","venue":null,"work_id":"ca29850b-c0f8-42b8-b4e7-7b215b306159","year":2015},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:9118807d11dcd6e810f6beac1ab11a9e0b108d5a45391ed4e217ad0ae2a4aed7","observation_id":"f72a2763-64f9-4b14-9c6d-facc1cff98a8","resolution":{"observed_at":"2026-07-09T18:06:26.304727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":"2303.08774","doi":"10.1002/tea.20265","metadata_source":"pith","pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GPT-4 Technical Report","venue":"cs.CL","work_id":"b928e041-6991-4c08-8c81-0359e4097c7b","year":2023},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:402d1910a4c7ec8c9a6a3dd87aea1726a1b7124174a6c65d2bfcb62e3d98ee89","observation_id":"695d09d6-e7ba-4a05-8391-90c3f117b8f5","resolution":{"observed_at":"2026-07-09T18:06:25.905464Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.307178Z","title":"Adain-based tunable cyclegan for efficient unsu- pervised low-dose ct denoising,","venue":null,"work_id":"d02ecbca-3a8c-48ad-8547-3dd2bb634bb3","year":2021},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:62e12f8a501001b67e301972ac6f028d4baedd86f6f0b27aeff914055fcce491","observation_id":"5345c0f3-241c-4de3-a377-048cf101ac60","resolution":{"observed_at":"2026-07-09T18:06:26.308530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.319800Z","title":"Motiondiffuse: Text-driven human motion generation with diffusion model","venue":null,"work_id":"454d79e1-6cd9-4c53-8b01-5b8aba13ed19","year":2024},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:ef7fdf74e38b170b2068516ec539fbcfae1d1c3bfa005d24e8e3405de9dd909a","observation_id":"db0dcdc5-de94-4160-8404-f9efd23ec006","resolution":{"observed_at":"2026-07-09T18:06:26.320966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.293801Z","title":"Diffusion models in vision: A survey","venue":null,"work_id":"7da5facb-18aa-4269-b339-14cb00d64e56","year":2023},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:2fa2f9848b96481e17158dc831ab4a12bdd9069683a3bb3c301e64ebe01143ec","observation_id":"002bc721-2bd8-420d-8ead-03941c54c3d5","resolution":{"observed_at":"2026-07-09T18:06:26.295778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.296453Z","title":"Cross-domain weakly-supervised object detection through progressive domain adap- tation,","venue":null,"work_id":"8f178942-2393-4017-b8de-acd6531ed35f","year":2018},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:f26cc92b5466fb2bdb479e8c5ea9178bdadddc08a0e43e9126ed9b29b4e1cf62","observation_id":"b5e8967b-5978-4c10-baba-4f1bf28dfebf","resolution":{"observed_at":"2026-07-09T18:06:26.297701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.298214Z","title":"The pascal visual object classes (voc) challenge,","venue":null,"work_id":"7921b393-bf4e-4437-8147-20614e4e50db","year":2010},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:1ddc848ef0045716605c04ab724a6c2ce756058b255c7809de2078aa51c609e3","observation_id":"f4d76635-17bf-4bae-b5f8-32783972282d","resolution":{"observed_at":"2026-07-09T18:06:26.299376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.299970Z","title":"The cityscapes dataset for semantic urban scene understanding,","venue":null,"work_id":"87f15fbc-4052-4d55-b4b9-30e12401cbac","year":2016},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:adab76b55aa02c5eb09174d9eb7a052a678bff88cce7d84e698a35983c90756a","observation_id":"6f58b796-ce9f-416a-9a5d-6794d1e3c38f","resolution":{"observed_at":"2026-07-09T18:06:26.301148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.305267Z","title":"Acdc: The adverse conditions dataset with correspondences for semantic driving scene understanding,","venue":null,"work_id":"fc737260-639b-4427-ba43-d38c49810a38","year":2021},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:37865506ba30e39430e0c145362a05212cd87f55d1ee54090df101b6265caccb","observation_id":"5dbc5cf0-a11c-4c48-bf0d-9f6346c95ebf","resolution":{"observed_at":"2026-07-09T18:06:26.306619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.309101Z","title":"Playing for data: Ground truth from computer games,","venue":null,"work_id":"f118951f-d1e3-4c3d-aa2e-6a97c935611c","year":2016},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:3bb496b7621bdd46cabaef691f8dbdb0f39e23fd5f602e2ebc4aa0cf697ccf05","observation_id":"731ef003-a93f-4425-9012-9e6fa332a096","resolution":{"observed_at":"2026-07-09T18:06:26.310324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.292131Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks,","venue":null,"work_id":"6633dbe0-ac81-4c68-821e-661d83a534bf","year":2015},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:4a22bede9e93f3287896c5cd9f8abc16d501fefaa2420120fca811e4c16e8a7e","observation_id":"88a14a28-1857-4768-9806-b01ef3a82966","resolution":{"observed_at":"2026-07-09T18:06:26.293237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.286720Z","title":"Switchable whitening for deep representation learning,","venue":null,"work_id":"10d3a0b4-525f-4107-bf03-05103ea4e85c","year":2019},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:70fb8883870f15073862df6d8fa1e78d52b828a5392817a9a72eadf11c822309","observation_id":"da1c436f-e3a2-4603-8ca5-42be2e34a0b0","resolution":{"observed_at":"2026-07-09T18:06:26.287855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.283178Z","title":"Robustnet: Improving domain generalization in urban-scene segmentation via in- stance selective whitening,","venue":null,"work_id":"2427b8d2-9b41-44e9-acd9-a76af04920c2","year":2021},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:eec4c1612453c05c59c8b710fcb6ce00fbab2a38888867af845c64b216d6a16e","observation_id":"223113bc-bafc-4235-8349-fc3cfe81efdb","resolution":{"observed_at":"2026-07-09T18:06:26.284535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.285021Z","title":"Srcd: Se- mantic reasoning with compound domains for single-domain generalized object detection,","venue":null,"work_id":"2773d72c-8b65-401c-b613-dd64893e83e9","year":2024},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:b9ac1c796a35baf5b6826db5e39dad11464b12911f34bc23729add2b80206cbf","observation_id":"66e2f6b3-cce8-4992-b126-4bf018787048","resolution":{"observed_at":"2026-07-09T18:06:26.286200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.288419Z","title":"Yolov10: Real-time end-to-end object detection,","venue":null,"work_id":"4caede0c-d927-48db-93cc-942d23c8a03e","year":2024},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:82640693a14eb1fa29a822d066241b5d256ab3a994294a80f3dd8526b9e646b1","observation_id":"be294ccc-ebfe-4939-9603-d723f87a4720","resolution":{"observed_at":"2026-07-09T18:06:26.289641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T11:37:03.559815Z","title":"Diffusiondet: Diffusion model for object detection,","venue":null,"work_id":"3ee64d71-3e3a-4432-a761-489f61241621","year":2023},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:30a96f776118883bd19f6421f559c590b209fae346afe351fc095bff5f8207b2","observation_id":"f547090a-5f0e-4639-ad42-cd9249e61b77","resolution":{"observed_at":"2026-07-09T18:06:26.291654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.310879Z","title":"Grounded language-image pre- training,","venue":null,"work_id":"87e0645a-b894-4305-aa74-c716300849e6","year":2022},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:659b98521cfba471e6a60db50a274ef33ae6738150a1ef015615be3c4e9ee480","observation_id":"326695e0-2df1-4b26-91dc-1513a9d1f9aa","resolution":{"observed_at":"2026-07-09T18:06:26.312102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.351063Z","title":"Phrase grounding-based style transfer for single- domain generalized object detection,","venue":null,"work_id":"5634d71e-af4a-4081-b779-0522f0c9de25","year":2025},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:eae3dd2afe1a47b5492b690c4db25eb9457baa12106637c460f0250a4f77f7fb","observation_id":"b1e00390-4522-496d-bee6-095e9ba913ea","resolution":{"observed_at":"2026-07-09T18:06:26.352327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.277307Z","title":"Dino: Detr with improved denoising anchor boxes for end-to-end object detection,","venue":null,"work_id":"bb967ac5-036d-4eae-9750-d603082f9a21","year":2023},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:7a2c6d6f3b595ec1f635cfd29dccb0a74060f425dbebab3126e239eae118746f","observation_id":"bd00f464-05c0-4ded-93d5-a0128c9945a3","resolution":{"observed_at":"2026-07-09T18:06:26.278689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.321446Z","title":"Towards single- source domain generalized object detection via causal visual prompts,","venue":null,"work_id":"2b0cc92b-9940-492e-953c-907682ff59e4","year":2025},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:39c39b3ef567aaaba89eef4ce8bbb4b9ff0ebdbc5befbb677354b41993ec2f90","observation_id":"3955e473-4fc2-451e-908a-1e5398e179bb","resolution":{"observed_at":"2026-07-09T18:06:26.322666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.338722Z","title":"Towards robust object detection invariant to real-world domain shifts,","venue":null,"work_id":"21610878-2172-4371-a0ad-55715884dfa3","year":2023},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:31fe4a16e8440747826d004975bff9209111c2712405eac0819780909fbd7db3","observation_id":"01da2f49-4ecd-461b-9b57-b0f475c1c4b1","resolution":{"observed_at":"2026-07-09T18:06:26.339928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.335129Z","title":"Clipstyler: Image style transfer with a single text condition,","venue":null,"work_id":"9929e5b6-ee2d-428b-a073-9c51179b53e2","year":2022},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:fc32897611465c6728a53b924684c91fbb4c40acf735f17809111408d8b362fb","observation_id":"94aebd7a-cb4d-420c-a14e-081cd9608ea5","resolution":{"observed_at":"2026-07-09T18:06:26.336288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-09T18:06:26.336822Z","title":"Unified language-driven zero- shot domain adaptation,","venue":null,"work_id":"b1f821e4-84fc-4bb7-9abd-19b6801175c4","year":2024},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:0fd79dc32842e91bb15f9407506fcbe52b900895221d9eb694a5efe3a5ec20da","observation_id":"fa259a01-4af3-4b40-94eb-c15fa97ae5fa","resolution":{"observed_at":"2026-07-09T18:06:26.338086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03267","last_updated":"2026-05-01T23:55:43Z","snapshot_observed_at":"2026-08-15T00:17:32.875866Z","submitted_at":"2025-12-19T07:05:38Z","title":"OpenAI GPT-5 System Card","version":2},"cited_work":{"arxiv_id":"2601.03267","doi":"10.48550/arxiv.2601.03267","metadata_source":"pith","pith_arxiv_id":"2601.03267","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"OpenAI GPT-5 System Card","venue":"cs.CL","work_id":"ca87689a-0d29-4476-b504-b65dbbb08af4","year":2025},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"cited_paper":"/paper/2601.03267","citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:af679874529326500bc24dfa83bcaa483e7d13055a897d9553b03a55415d419f","observation_id":"b24ddd9f-3757-45e9-8247-273ad1e6d6a7","resolution":{"observed_at":"2026-07-09T18:06:25.902612Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-10T00:38:24.809631+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-10T00:38:24.809631+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":"2312.11805","doi":"10.1038/nrn2888","metadata_source":"pith","pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Gemini: A Family of Highly Capable Multimodal Models","venue":"cs.CL","work_id":"83f7c85b-3f11-450f-ac0c-64d9745220b2","year":2023},"citing_paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-09T17:59:03.728392Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2607.07192"},"observation_digest":"sha256:43f4d45b7d476d922cce2ab6e485717108193d6644c1e4f42b516d4acea505e9","observation_id":"6b90d257-a583-44bc-9acb-312ec2e264da","resolution":{"observed_at":"2026-07-09T18:06:25.910176Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.07192","last_updated":"2026-07-08T09:27:15Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T10:36:17.843137Z","submitted_at":"2026-07-08T09:27:15Z","title":"Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":4,"verified_fuzzy":46},"total_outbound_references":50},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2607.07192."}