{"as_of":"2026-08-07T12:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e52b0f430bb88d25403f9b31ac343a980b02cf6b643ce5cce8e1756863629360","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":"2026-08-06T21:30:30.402106Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-20T10:47:38.553051Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-20T10:48:12.692711Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"cited_work":{"arxiv_id":"2506.24063","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.24063","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Continual adaptation: Environment-conditional param- eter generation for object detection in dynamic scenarios","venue":null,"work_id":"24a5892b-8680-402c-b9ed-70e251c4f2cb","year":2025},"citing_paper":{"arxiv_id":"2605.18608","last_updated":"2026-05-18T16:18:17Z","snapshot_observed_at":"2026-07-06T23:29:29.105126Z","submitted_at":"2026-05-18T16:18:17Z","title":"Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-20T10:47:38.553051Z"},"links":{"cited_paper":"/paper/2506.24063","citing_paper":"/paper/2605.18608"},"observation_digest":"sha256:33efbe2ef2771acd91ea5f1cc63f3113ef072da358868ea80a7abc86abe4a6ff","observation_id":"b2d7ed19-4e00-4ce8-ae30-ff80a5eff4b7","resolution":{"observed_at":"2026-05-20T10:48:12.694353Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.24063/citation-record","integrity":"/paper/2506.24063/integrity","json":"/paper/2506.24063/citation-record.json","paper":"/paper/2506.24063"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.974233Z","title":"Hyper- style: Stylegan inversion with hypernetworks for real image editing","venue":null,"work_id":"dea195c6-39e0-4d64-9c17-e40569f4d462","year":2022},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:27.540275Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:4c53056dd2ccf9156e82b6bad63d4c89723cfcb6f9af1a1bbcbf43a527115d98","observation_id":"e19f0634-e2ba-450e-8e49-308fe06e85ee","resolution":{"observed_at":"2026-08-06T21:30:32.979874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.852220Z","title":"A theory of learning from different domains.Machine learn- ing, 79:151–175, 2010","venue":null,"work_id":"d86a1086-da3f-40ac-97bf-8c16d1799e61","year":2010},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:27.605206Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:a681ee928c45896053b573dac0092f958f68adff612f325af8874a055f489932","observation_id":"79221374-e5aa-4bbe-9672-df6a6ab0db2d","resolution":{"observed_at":"2026-08-06T21:30:32.857891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:27.686911Z","title":"End-to- end object detection with transformers","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:27.686911Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:8d4b50fd39b3f978cf941d60eff119b4bc025d7d34ea8f251427b3f3e29b85d1","observation_id":"fbc75d36-fb08-41eb-80bc-c501efa16aad","resolution":{"observed_at":"2026-08-06T21:30:27.686911Z","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":"2026-08-06T21:30:32.827180Z","title":"Domain generalization by solving jigsaw puzzles","venue":null,"work_id":"4002935b-6996-44a0-b177-03294d2b46a1","year":2019},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:27.761190Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:35fc3d6faed2c6e29d5464a24aca64b9a876102083df5bba1f532557a1976703","observation_id":"0c4b191b-9416-40fb-8159-6587392908c6","resolution":{"observed_at":"2026-08-06T21:30:32.831917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.17937","last_updated":"2023-03-31T10:04:44Z","snapshot_observed_at":"2026-07-06T15:10:26.549542Z","submitted_at":"2023-03-31T10:04:44Z","title":"STFAR: Improving Object Detection Robustness at Test-Time by Self-Training with Feature Alignment Regularization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.17937","snapshot_observed_at":"2026-08-06T21:30:27.830912Z","title":"Stfar: Im- proving object detection robustness at test-time by self- training with feature alignment regularization.arXiv preprint arXiv:2303.17937, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:27.830912Z"},"links":{"cited_paper":"/paper/2303.17937","citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:ce8d5f3e8177ab463b112308d8e4a6bea582c19f4ca8a514f8260eb39e849cbd","observation_id":"d9419a50-536b-45af-9c80-600585215d91","resolution":{"observed_at":"2026-08-06T21:30:27.830912Z","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":"2026-08-06T21:30:32.813790Z","title":"Robustnet: Improving domain generalization in urban-scene segmentation via in- stance selective whitening","venue":null,"work_id":"bd751fd8-5620-4cd1-8ee9-a84ef7cf3ccf","year":2021},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:27.883900Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:ea1c064e3a3396b252934b3f2b8dca1b7cdf72e05e062f10059ed70cb62731c1","observation_id":"49946fee-443a-4c29-a44b-976baaabcce5","resolution":{"observed_at":"2026-08-06T21:30:32.818274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.798666Z","title":"Hyperdiffusion: Diffusion models for neural im- plicit fields via hypernetworks","venue":null,"work_id":"f33238a6-dd78-41f4-98b2-80425e314427","year":2023},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:27.945515Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:a2d23184c2768f2db63e96bfc6e8161f62532105b8e4bf1edf95dc286a0ebd37","observation_id":"22e3fae0-c5da-4ea6-8107-4bbdf54c1943","resolution":{"observed_at":"2026-08-06T21:30:32.804349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.784059Z","title":"Cloud-device col- laborative adaptation to continual changing environments in the real-world","venue":null,"work_id":"d38057e0-87e4-4051-bf14-aa5fb0ae2f79","year":2023},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.019647Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:81185ba579d320d3760adc1bb7518c122edda3348e639fb1e6847dd44566f8c0","observation_id":"acfe12d1-c5fe-4e7d-957d-e1c171e8c765","resolution":{"observed_at":"2026-08-06T21:30:32.788760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.768683Z","title":"Unsupervised domain adaptation by backpropagation","venue":null,"work_id":"6813de81-432c-4fb4-a53f-f6fcbd20aefd","year":2015},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.069483Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:5beb8eecfad2f7452d1fa228193d8802d7b57bd1ed76a614df0dc0cc1221e887","observation_id":"44dbeaeb-f5a1-42cf-aeec-099168534559","resolution":{"observed_at":"2026-08-06T21:30:32.773302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.754838Z","title":"Domain-adversarial train- ing of neural networks","venue":null,"work_id":"62e90faf-5351-431f-a630-968cc6f04ac3","year":2016},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.132044Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:2c0b677983e38cfeb211eef8ae4e395f494bf6db12b617c9ea28229a4e3da1ae","observation_id":"dbd3649d-b5d5-4254-b5a5-0689195dc486","resolution":{"observed_at":"2026-08-06T21:30:32.759274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.739914Z","title":"Domain generalization for object recog- nition with multi-task autoencoders","venue":null,"work_id":"2b99fa4b-f71b-4302-a3e6-25ab912be838","year":2015},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.201983Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:09cc0491369ec0f026ddb2a8af06e5cfbdcf16e6c028453fc060e5c13474772f","observation_id":"14ccf5d1-ed93-4d62-ad13-44a8554f7fee","resolution":{"observed_at":"2026-08-06T21:30:32.745070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.724757Z","title":"Hypernetworks","venue":null,"work_id":"49897de5-bd8e-4ab2-8b89-7aa3e171b51f","year":2017},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.278934Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:6e31074d17353107c1beab49b95dc7094b4c19ec46eb3f41d743ff4715dde621","observation_id":"e73364a8-f977-4684-9b48-64e2ebdd5cac","resolution":{"observed_at":"2026-08-06T21:30:32.729499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.708947Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"08c25832-8a87-4312-b2f6-8d21398387e2","year":2016},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.334101Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:eeff8832bbe7d3c6f0d67b228d2331117116fe871eba84ebd9bdc1ee200c55e2","observation_id":"e50af39b-84e7-4806-9968-3e660729d325","resolution":{"observed_at":"2026-08-06T21:30:32.713750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.694570Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":"55a713a5-66e4-4464-98e4-5270f636ae74","year":2022},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.431347Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:d00b9d7c5a3650109a0350e8ecf0c8c4bbb6c42f8b8eddb67a4331f4f7857231","observation_id":"c94987d3-dd8e-4d74-9c93-037e55606cf1","resolution":{"observed_at":"2026-08-06T21:30:32.699095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.676613Z","title":"It- erative normalization: Beyond standardization towards effi- cient whitening","venue":null,"work_id":"166cc181-9a9a-4282-b5e9-7e8b730982d3","year":null},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.483510Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:db55040839ad4931ba56cec5879dc362fcefdb6c4bbb69a8ffe841914a092725","observation_id":"de648801-0215-494b-a174-f4dfa6857c9c","resolution":{"observed_at":"2026-08-06T21:30:32.683469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.663230Z","title":"Overcoming catastrophic forgetting in neu- ral networks","venue":null,"work_id":"9d97aba0-41e9-4e6c-89b2-ff540852fae5","year":2017},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.554616Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:9dfc02ff1c9b991f2980580990bb4b34812bb8a20d208e054d5180fc4ab211a5","observation_id":"0fc5c027-f8c5-4247-8ba7-a7c38adfb8f5","resolution":{"observed_at":"2026-08-06T21:30:32.667393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.649005Z","title":"Universal source-free domain adaptation","venue":null,"work_id":"fe4915bc-19ee-4b0d-8f41-eb6ed3a61d6d","year":2020},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.640791Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:b0fb3aa2a44eb67701e47c3844efcde3e1240aa22e629d8ff48a7d66931fb469","observation_id":"1490d191-f8f6-429c-909b-86fbaade9982","resolution":{"observed_at":"2026-08-06T21:30:32.653548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:28.729877Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.729877Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:9c219f88e6c8a4af361dde3b8fd5fc16729b8229c7893abac8fb3973af383e0b","observation_id":"0c1ba044-4f98-4528-8c81-a225295e7c23","resolution":{"observed_at":"2026-08-06T21:30:28.729877Z","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":"2026-08-06T21:30:32.626472Z","title":"Bird’s-eye-view scene graph for vision-language navigation","venue":null,"work_id":"d13646c6-2615-4227-8be4-1ecb5ce6672e","year":2023},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.812358Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:73f63c8a63e73941064a263a5dc8b27410751434343f738078616782a24af2f0","observation_id":"8a4a924a-a734-4038-9dd3-e890885093f3","resolution":{"observed_at":"2026-08-06T21:30:32.630620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.613097Z","title":"Vision-language nav- igation with energy-based policy","venue":null,"work_id":"a9c6fc14-5a6a-4522-a81a-83057a6d9e50","year":2024},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.874630Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:d82fdf1e1dd3412249d6a1c318331fece77f725dcb8b97493b4319db37666f08","observation_id":"4c1fe1f8-66a3-461c-9aa1-b3df57e81e68","resolution":{"observed_at":"2026-08-06T21:30:32.616939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.599095Z","title":"V olumetric envi- ronment representation for vision-language navigation","venue":null,"work_id":"af74e3fe-ec01-4159-829a-7923edf12d8e","year":2024},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:28.943986Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:98eb5e5b7a1df73b4ecf30897509cc6eeafc2b2698334b8459aa6119a5fb39c3","observation_id":"e0ffb099-1a40-4093-a4bc-e60add6fc6b0","resolution":{"observed_at":"2026-08-06T21:30:32.603858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:29.014501Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.014501Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:e298e075dabad7b4bccb0b9c8235fc407ec49bbf695294e6e208ab016723fedb","observation_id":"6861dc72-8b22-4f13-aa30-75bda4acb064","resolution":{"observed_at":"2026-08-06T21:30:29.014501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.07484","last_updated":"2020-03-31T08:42:46Z","snapshot_observed_at":"2026-08-03T06:25:53.775445Z","submitted_at":"2019-07-17T12:51:10Z","title":"Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.07484","snapshot_observed_at":"2026-08-06T21:30:29.063038Z","title":"Benchmarking ro- bustness in object detection: Autonomous driving when win- ter is coming","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.063038Z"},"links":{"cited_paper":"/paper/1907.07484","citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:cda74f48d5febf1da248080a7b84abb83b38d7b518c1ad40127ddf1241859ea0","observation_id":"f424fd2b-1604-4d49-9dd5-287012155d35","resolution":{"observed_at":"2026-08-06T21:30:29.063038Z","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":"2026-08-06T21:30:32.576097Z","title":"The norm must go on: Dynamic unsuper- vised domain adaptation by normalization","venue":null,"work_id":"99cb2c9b-d7d0-43a4-9fdd-02582c3fe1e0","year":2022},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.171725Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:433e36ccb9e6c1cf152d86b0f4a2db60b6d1f3812099c2a18acecad7864523a6","observation_id":"2299fe72-3bf4-4cbb-b90c-4d40c8c7a6cf","resolution":{"observed_at":"2026-08-06T21:30:32.580327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.561736Z","title":"Act- mad: Activation matching to align distributions for test- time-training","venue":null,"work_id":"7200af81-bd7a-4c40-954d-15fee1bed6a7","year":2023},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.237343Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:bc2ae9a2dc446ea7d7604c9be9f10c1f892da99550de6ad977f69ded3e1ff33b","observation_id":"90469039-878f-4b16-a1da-fb42d9069cae","resolution":{"observed_at":"2026-08-06T21:30:32.566182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.548824Z","title":"Domain generalization via invariant fea- ture representation","venue":null,"work_id":"bcb6460c-9383-406f-9ad7-9a80e688a8d4","year":2013},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.330454Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:bc02526581e804ef733889a0e963b9f29ac75318e86411899d6f72ccb2845f7c","observation_id":"868d6345-0852-4757-b551-c5012fd24f7e","resolution":{"observed_at":"2026-08-06T21:30:32.553033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.535266Z","title":"Image to image transla- tion for domain adaptation","venue":null,"work_id":"82d9b740-f974-48cb-a7e0-811d63481c48","year":2018},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.376418Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:26eda0910facd08918988aaa6f3a034bc625c814a45bf3dd9a9c5625f6dad5d7","observation_id":"ef35d597-8e8a-488f-84b3-73dd1cc01ea8","resolution":{"observed_at":"2026-08-06T21:30:32.539514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.12400","last_updated":"2023-02-24T02:03:41Z","snapshot_observed_at":"2026-07-06T14:55:19.727805Z","submitted_at":"2023-02-24T02:03:41Z","title":"Towards Stable Test-Time Adaptation in Dynamic Wild World","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.12400","snapshot_observed_at":"2026-08-06T21:30:29.437581Z","title":"Towards stable test-time adaptation in dynamic wild world","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.437581Z"},"links":{"cited_paper":"/paper/2302.12400","citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:47132dad752dbabbf8fc47ba80309af336f5fae7199be886f870ef8521341264","observation_id":"cad55137-661e-48b4-bd59-cfb946fa4000","resolution":{"observed_at":"2026-08-06T21:30:29.437581Z","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":"2026-08-06T21:30:32.522873Z","title":"Two at once: Enhancing learning and generalization capacities via ibn-net","venue":null,"work_id":"bcaa19ac-d43d-4522-99fd-83523550eee8","year":2018},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.494255Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:6f31e97889febcd428dcbb9c2e7cc294f589b004e11b72924e7d7ae4b32bff29","observation_id":"236216f0-59e9-44cb-b5e4-c25d2c996a40","resolution":{"observed_at":"2026-08-06T21:30:32.526635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.510028Z","title":"Switchable whitening for deep representa- tion learning","venue":null,"work_id":"9b6440e5-aa9c-41a0-8b2b-0ba47885a0e9","year":2019},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.547422Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:d88599ec108e51d11a67c2c3d726245277ccb6eeead381ecceff087cf1a6dc16","observation_id":"360689dd-358a-4713-aaff-b19528c8173e","resolution":{"observed_at":"2026-08-06T21:30:32.514073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.12892","last_updated":"2022-09-26T17:59:58Z","snapshot_observed_at":"2026-08-07T09:30:10.117486Z","submitted_at":"2022-09-26T17:59:58Z","title":"Learning to Learn with Generative Models of Neural Network Checkpoints","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.12892","snapshot_observed_at":"2026-08-06T21:30:29.622367Z","title":"Learning to learn with genera- tive models of neural network checkpoints","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.622367Z"},"links":{"cited_paper":"/paper/2209.12892","citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:774f8e8f6bf4a07d9f3297de0f9fce57dd6379d7b31eaa0a09cece6f1496d905","observation_id":"c9d4cddd-ffbc-4dae-ac6d-f5fdedd5c3d6","resolution":{"observed_at":"2026-08-06T21:30:29.622367Z","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":"2026-08-06T21:30:32.496557Z","title":"Deep convolutional neu- ral networks for image classification: A comprehensive re- view","venue":null,"work_id":"2c5fddcd-34df-45c2-b235-479462ada2cd","year":2017},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.677055Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:26a3f3e14e83f1ecaf0404bdfc457601d4c886f1c7bdc53985866598842e9cf9","observation_id":"604bba8f-b832-4502-9a2e-b8bb2bba02ec","resolution":{"observed_at":"2026-08-06T21:30:32.501140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.483577Z","title":"Fully test-time adaptation for object detection","venue":null,"work_id":"3974decb-111d-40e6-9b69-bf0a273fada6","year":2024},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.707233Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:ca4eadee888b050eee0016a22f26919950dabc515f0303b844dc35e340855cb6","observation_id":"d4297ef3-dc58-4644-a1ba-924f8ba780e5","resolution":{"observed_at":"2026-08-06T21:30:32.487748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.469241Z","title":"Generate to adapt: Aligning domains using generative adversarial networks","venue":null,"work_id":"75308e4c-0146-4529-885d-a81d7b503395","year":2018},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.740829Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:4e0ef47a137bc9063f5b5b92e5f4bac889da2b0a3708b50588894197da051c71","observation_id":"4dd729cb-4123-4aa0-9113-dfc7f48d5285","resolution":{"observed_at":"2026-08-06T21:30:32.473977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.455801Z","title":"Improving robustness against common corruptions by covariate shift adaptation","venue":null,"work_id":"5e9318d4-dc0c-4fab-b285-468c324724cd","year":2020},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.801496Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:82883dae567d2077b924b28c86602717e87df6e6c5749624eb4ee27e77af507b","observation_id":"4976b7dc-23e9-4197-a48e-3cc312aeea22","resolution":{"observed_at":"2026-08-06T21:30:32.459738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.442036Z","title":"Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization","venue":null,"work_id":"d40035e1-d927-4c0f-af16-4447623aaf3c","year":2023},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.865603Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:653e98925046f29dc70531ea2048a6d67f1a0fb2424981d6b23b503668241178","observation_id":"6e3af2e0-3f6e-4baa-8b3e-c192dc668fba","resolution":{"observed_at":"2026-08-06T21:30:32.446614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.428329Z","title":"Mixture regression for covariate shift","venue":null,"work_id":"5e49677b-f8e5-4593-897e-e0b73260f792","year":2006},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.910974Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:d9902edc634cdbc82434511558d36d4df805a2178a6d90fc76ed6c86b149e37b","observation_id":"227cb2e5-f5a7-44e3-90f9-10c1966cf336","resolution":{"observed_at":"2026-08-06T21:30:32.432427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.375746Z","title":"Shift: a synthetic driving dataset for continuous multi-task domain adaptation","venue":null,"work_id":"5d458cc9-28c9-4215-8d26-93c06ccfb7f4","year":2022},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.952031Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:6f77f93cb7e771d328daa5233bbb44cee0a7ae5a7d7da8a73cf44df02b7c05b1","observation_id":"927d40a7-d1d5-4799-beb3-a0a5182ba597","resolution":{"observed_at":"2026-08-06T21:30:32.417486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:32.092444Z","title":"Tent: Fully test-time adaptation by entropy minimization","venue":null,"work_id":"46280338-52be-4ea5-87b7-6fb329adf97c","year":2020},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:29.993778Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:679fa17f34a9a47fedff8adea2f2cd99d3d9bd7a6a5eea6c0639232b441fbb74","observation_id":"bbe5b9bc-35a4-41a0-8ef1-a5bc112d20ed","resolution":{"observed_at":"2026-08-06T21:30:32.257367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:31.857810Z","title":"Continual test-time adaptation","venue":null,"work_id":"ba9d4e4e-1d1e-40c1-95e8-9490ebc0f456","year":2022},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:30.069872Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:81c0efa84e4f832c9b3da27def5b2647f9240c59a2746a5c3ce357fe04723f65","observation_id":"413f8f74-36ed-48d1-98e2-f8786b1e5470","resolution":{"observed_at":"2026-08-06T21:30:31.927013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:31.704618Z","title":"Single-domain generalized ob- ject detection in urban scene via cyclic-disentangled self- distillation","venue":null,"work_id":"b74b0eef-493e-4b70-a72d-dcca808c41ff","year":null},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:30.113515Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:395987c4184d33e5dcfad87cd0cf4c13eed7ebdb214a88d4ba4a55199e9b5b37","observation_id":"1fd936d1-3fde-4c74-b5a8-82a66b0e431c","resolution":{"observed_at":"2026-08-06T21:30:31.778663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:31.435238Z","title":"Vector-decomposed disentanglement for domain- invariant object detection","venue":null,"work_id":"c96d69a6-2995-4ef2-a5ca-0c70c7196897","year":2021},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:30.155038Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:c891dfbbb0398035f5c0bfb4df94c022a6a8acfcbe1fe2bb1b26744782a49c09","observation_id":"9e5b7ab6-fb50-4c1e-8d32-9b61fcd62c3e","resolution":{"observed_at":"2026-08-06T21:30:31.556306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:31.189244Z","title":null,"venue":null,"work_id":"e81705a3-c1d6-44c9-8eed-a5dbfaf2bc07","year":2024},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:30.211226Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:8e8c3dbcc3d8d10d7266d89bd2a80c19eff6bcf278d6cf7a58bc634419eb9b10","observation_id":"998ebcf5-5ea2-4962-88ab-d317d900cd2d","resolution":{"observed_at":"2026-08-06T21:30:31.310500Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:30.934526Z","title":"Spatio-temporal few-shot learning via diffusive neural network generation","venue":null,"work_id":"7a1c8f3a-e57c-4e71-a0ea-6a365cb54e1d","year":2024},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:30.273177Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:001285ac52d8c6fae7d8b354d8c04107f713cc96eca6dc538c54357d4fe1c239","observation_id":"48dc4213-eed9-4f5f-ad54-cdc7df416199","resolution":{"observed_at":"2026-08-06T21:30:31.054212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:30.660204Z","title":"Memo: Test time robustness via adaptation and augmentation","venue":null,"work_id":"22b0848b-e67b-4979-9e03-041921c6053c","year":2022},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:30.327155Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:ed5ac4810ca3c2225ffc146a3902fb617909e2444045d28ba0e0d25fff4b0300","observation_id":"0f128afb-5957-4d13-aa53-872fb73b3ee1","resolution":{"observed_at":"2026-08-06T21:30:30.802262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:30:30.532039Z","title":"Object detection in 20 years: A survey.Proceed- ings of the IEEE, 2023","venue":null,"work_id":"c3328c1c-4460-490b-8737-f6716543f1bd","year":2023},"citing_paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T21:30:30.402106Z"},"links":{"citing_paper":"/paper/2506.24063"},"observation_digest":"sha256:6c5c22b5231546b8a23f969f261fd1b2378168e8aae77e9644bb5826372fc1bb","observation_id":"4037e276-6b4d-47a3-b93b-391bcccedb28","resolution":{"observed_at":"2026-08-06T21:30:30.585530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.24063","last_updated":"2025-06-30T17:14:12Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T21:22:48.996276Z","submitted_at":"2025-06-30T17:14:12Z","title":"Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":38},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2506.24063."}