{"as_of":"2026-08-10T06:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e9eea8cc0a618b7655e98ed2154c60a61dade5ddb3121958366484b703d45724","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":"2026-08-07T14:04:45.598407Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2505.20041/citation-record","integrity":"/paper/2505.20041/integrity","json":"/paper/2505.20041/citation-record.json","paper":"/paper/2505.20041"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:04:52.848608Z","title":"Lightweight dual stream network with knowledge distillation for rgb-d scene parsing,","venue":null,"work_id":"288a9565-17dd-4e86-9268-329806c4e9e1","year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:40.955033Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:cebd54c4e915860c1d1ca54aa44707cdc5838e33b3db78254bc3625ca520a2bb","observation_id":"710fe060-8146-4793-8536-53a4658d3a8c","resolution":{"observed_at":"2026-08-07T14:04:52.945078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:52.708417Z","title":"SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection,","venue":null,"work_id":"7a551d0d-aced-4228-8694-b4a7bc974872","year":2020},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:41.078288Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:851d1288639433062f1cbbfd37cb2a578a87c48312d59000cf8dbbc07690c1b4","observation_id":"754279a3-e3e0-469c-ad13-46dbe5dd075d","resolution":{"observed_at":"2026-08-07T14:04:52.789104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:52.560554Z","title":"S 3M-Net: Joint learning of semantic segmentation and stereo matching for autonomous driving,","venue":null,"work_id":"3594cd33-ba6a-4fd7-bb58-f0056d1e5cc0","year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:41.250755Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:e74ea3ca014034128b45b3a953a7bf17636853b214e964731070d7f078e8fb94","observation_id":"1c13d608-b8fe-4985-bf2e-b6a009a726f2","resolution":{"observed_at":"2026-08-07T14:04:52.633218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:52.404986Z","title":"SNE-RoadSegV2: Advancing heterogeneous feature fusion and fallibility awareness for freespace detection,","venue":null,"work_id":"804e0c5e-951e-4d56-a38a-7956a9a91c08","year":2025},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:41.363456Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:5558ac7f4f33b64b845279a97ae5dce51b724635ae437f2c38a7ca2458d6e415","observation_id":"ba787d18-e95e-47dd-af51-4683076ff76d","resolution":{"observed_at":"2026-08-07T14:04:52.486640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:52.196371Z","title":"Road damage detection based on unsupervised disparity map segmentation,","venue":null,"work_id":"cf2f11d8-bc9c-4a6d-8638-e3241294f919","year":2020},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:41.478506Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:b5dec8afdf0947878c32758f9a21d1a66a13da9f2990b095e91d34de5655d344","observation_id":"d5fcdc19-0d2e-4acf-9fc4-337f735d6480","resolution":{"observed_at":"2026-08-07T14:04:52.296988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:51.926599Z","title":"Fully Convolutional Networks for Semantic Segmenta- tion,","venue":null,"work_id":"d0addee4-78bc-48d5-969d-c0fc3c2abbdc","year":2015},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:41.595917Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:d827d7e4ac18abb54c6a556d8ba7d3c4aa1c232d83687ab32ec9b51a88036993","observation_id":"1c40d962-a7e8-42a1-a5a7-fd689850b751","resolution":{"observed_at":"2026-08-07T14:04:52.057773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:51.693577Z","title":"Pothole detection based on disparity transformation and road surface modeling,","venue":null,"work_id":"db9f7453-b63e-42e2-8ff2-7a19934fe458","year":2020},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:41.709348Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:a4d757a5d73b75c524bf467f41a2a1e795bd4faafa46ba87c8c953c705945e29","observation_id":"2016a427-e6e6-4a74-8829-f614470cb48a","resolution":{"observed_at":"2026-08-07T14:04:51.811548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:41.851026Z","title":"RoadFormer+: Delivering RGB-X scene parsing through scale-aware information decoupling and advanced heteroge- neous feature fusion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:41.851026Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:16685fc782e8e28a0f714d66b02caa1d351e1597e465554821b3a33ecaa8f1b8","observation_id":"29dc038b-f190-4601-96a5-df8500b66fda","resolution":{"observed_at":"2026-08-07T14:04:41.851026Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-07T14:04:51.466922Z","title":"MFFENet: Multiscale feature fusion and enhancement network for rgb–thermal urban road scene parsing,","venue":null,"work_id":"ad62378e-a886-4d0c-bc83-c1a8687207e0","year":2021},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:42.081008Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:60bfd95ddcfbfd3f4136973dbe72ac21e1fe30c04258fecb15bd8ef56438d784","observation_id":"ca739441-6892-4b64-8e61-b66c2923899a","resolution":{"observed_at":"2026-08-07T14:04:51.556067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:51.202848Z","title":"MDNet: Mamba-effective diffusion-distillation network for rgb-thermal urban dense prediction,","venue":null,"work_id":"b6940cb3-fffc-4dbb-8e63-2e034c85bdef","year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:42.243794Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:97af4fb82eff37a726d454d1edea1a64a80a1f86fe3952995cbe8fe7e9007516","observation_id":"5746afc7-1d40-4a9e-a8a6-a86c4eb4134a","resolution":{"observed_at":"2026-08-07T14:04:51.344591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:50.891107Z","title":"FuseNet: Incorporating Depth into Semantic Seg- mentation via Fusion-Based CNN Architecture,","venue":null,"work_id":"f348562f-674a-4f01-873f-ae36d3e3478d","year":2017},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:42.396324Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:a1929904469025b1a36b4fc2f4f785b8782f0e97b232f0ea052c59fa9ceec13c","observation_id":"f7ed54e7-a460-4867-92c8-6b7dc96ef52b","resolution":{"observed_at":"2026-08-07T14:04:51.069392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:42.562978Z","title":"RoadFormer: Duplex Transformer for RGB-Normal Se- mantic Road Scene Parsing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:42.562978Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:985ceb9caab8a670edfbd0a58db4981836716e5f3f6a06cb3f2df1a78eee8585","observation_id":"cbd05918-d2de-4990-ab2a-d8555d844293","resolution":{"observed_at":"2026-08-07T14:04:42.562978Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00360","last_updated":"2023-12-04T04:38:17Z","snapshot_observed_at":"2026-07-06T16:55:29.501535Z","submitted_at":"2023-12-01T05:50:44Z","title":"Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00360","snapshot_observed_at":"2026-08-07T14:04:42.649660Z","title":"Efficient multimodal semantic segmentation via dual- prompt learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:42.649660Z"},"links":{"cited_paper":"/paper/2312.00360","citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:5e8930f243d7b0a9448dcd68d02e642f615099e3bbc21be8c164b7c9c019b68d","observation_id":"4e74b6fc-6280-4516-a533-9be7d3afd704","resolution":{"observed_at":"2026-08-07T14:04:42.649660Z","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":"2026-08-07T14:04:50.621024Z","title":"Depth-assisted semi-supervised rgb-d rail surface defect inspection,","venue":null,"work_id":"fdbcb4cf-2c59-471a-a975-afd0e9549b49","year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:42.737324Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:494dedc61a4dac1757ffb854dd3787045ec433485899a24e973e8fad4b749ca3","observation_id":"bd8132df-4a14-40d6-be72-07eddc9a5faa","resolution":{"observed_at":"2026-08-07T14:04:50.741521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10777","last_updated":"2025-01-09T04:57:35Z","snapshot_observed_at":"2026-07-06T19:33:16.949210Z","submitted_at":"2024-10-14T17:49:27Z","title":"UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation","version":2},"cited_work":{"arxiv_id":"2410.10777","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.10777","snapshot_observed_at":"2026-08-07T14:04:45.812542Z","title":"UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation","venue":"cs.CV","work_id":"d24e9408-9ffd-40c1-80e8-ce066661b9ca","year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:42.892854Z"},"links":{"cited_paper":"/paper/2410.10777","citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:e1faac02f1a936476c2f1938875e54eed3c5fea6e8062a0e5fd767766c354892","observation_id":"155274cc-f452-450f-af64-dfc87a81c337","resolution":{"observed_at":"2026-08-07T14:04:45.875288Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:50.439436Z","title":"Semi-supervised semantic segmentation needs strong, high-dimensional perturbations,","venue":null,"work_id":"ae924f80-24fe-4d07-b2a0-9b746f92d711","year":2019},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:42.981922Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:ed915e0411796604ad8beb5d96a7a1c0b3fb98917318d9b519c4f8f8318675bd","observation_id":"a67015d5-1c13-4934-aab4-ec10eb4bdba8","resolution":{"observed_at":"2026-08-07T14:04:50.535529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.04552","last_updated":"2017-11-29T14:51:40Z","snapshot_observed_at":"2026-07-06T05:55:22.966528Z","submitted_at":"2017-08-15T15:21:53Z","title":"Improved Regularization of Convolutional Neural Networks with Cutout","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.04552","snapshot_observed_at":"2026-08-07T14:04:43.061828Z","title":"Improved regularization of convolutional neural networks with cutout,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:43.061828Z"},"links":{"cited_paper":"/paper/1708.04552","citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:c8f43f74955681b602dc032a5b86746dc965493789562c1c9b54e2ebe2908972","observation_id":"16126050-494c-4881-a46d-90c402dfc289","resolution":{"observed_at":"2026-08-07T14:04:43.061828Z","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":"2026-08-07T14:04:50.172907Z","title":"Cutmix: Regularization strategy to train strong classifiers with localizable features,","venue":null,"work_id":"9a1e4560-306c-4e50-a76b-2fd5d36cca76","year":2019},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:43.148025Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:5cf41379219150b179c4cbed8f4af88ca414ea6ea891a99c9f01f342db7553a6","observation_id":"6c1bc66f-b498-4b14-a4df-56591b32faf2","resolution":{"observed_at":"2026-08-07T14:04:50.316573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:49.925971Z","title":"Revisiting weak-to-strong consistency in semi- supervised semantic segmentation,","venue":null,"work_id":"cb3a095b-7076-41c3-9deb-9608e6d9e8eb","year":2023},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:43.255073Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:ed22235c5c45742371e41f4bcdcdff233efddcf0fdab6409d566bf244a075297","observation_id":"a7e8c86a-606a-49ed-9a99-d1471556e5b3","resolution":{"observed_at":"2026-08-07T14:04:50.019428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:49.688210Z","title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence,","venue":null,"work_id":"69d45b3e-2570-49a7-ba28-942da85d618d","year":2020},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:43.325552Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:3d3e5a66d80b494dac0b0f4de3f3a3ad2cecd0d6944e9862c2210b74b36ae5bb","observation_id":"01711e75-00b9-4a93-8714-018720834800","resolution":{"observed_at":"2026-08-07T14:04:49.786099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:49.437958Z","title":"Dinov2: Learning robust visual features without supervision,","venue":null,"work_id":"ea0e9479-bff9-463d-8479-fb1a1ec7abaf","year":2023},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:43.413690Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:45460291437be2624e343dba999bb804b66fbe5b6823f85502dd4f4ab886a9bd","observation_id":"a803312c-0507-44f3-9ac3-665af39d0b28","resolution":{"observed_at":"2026-08-07T14:04:49.574659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:49.246933Z","title":"Vision transformers for dense prediction,","venue":null,"work_id":"fcaa7582-0b1b-4f8f-83c2-1274438463c5","year":2021},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:43.499374Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:87046800f18042910a58190beb5923fabf15c841dfa5a85d629af52ae1830506","observation_id":"8fd6c249-5673-48f8-b13e-3dcb71bebc37","resolution":{"observed_at":"2026-08-07T14:04:49.328470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:49.070602Z","title":"Self-enhanced feature fusion for rgb-d semantic segmentation,","venue":null,"work_id":"41f0c4f9-e51b-4feb-a351-0c8bd6715f19","year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:43.595252Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:f8520b63625206395492f92a4ccf6baaea07381e53d6ddc2582ea95a80ad24a7","observation_id":"f3bc548c-e0fa-451e-a7a1-ba4fac7e0947","resolution":{"observed_at":"2026-08-07T14:04:49.156159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:48.908179Z","title":"DFormer: Rethinking RGBD representation learning for semantic segmentation,","venue":null,"work_id":"fc270e87-6db6-479a-b531-5beab6ed3b0c","year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:43.731722Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:bd3f9e0c67f3ae88e7e58360703bca24323c9286144b76fe163a14f6db231e00","observation_id":"6caed587-579d-45e8-9958-6c389627e23e","resolution":{"observed_at":"2026-08-07T14:04:48.987672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:43.817739Z","title":"Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:43.817739Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:579b0a30153098e06a985ef259812def77267e10c982dadf4806c4d1343e3ece","observation_id":"6c985786-d438-49fc-aa45-dc07815391de","resolution":{"observed_at":"2026-08-07T14:04:43.817739Z","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":"2026-08-07T14:04:48.756309Z","title":"Frnet: Feature reconstruction network for rgb-d indoor scene parsing,","venue":null,"work_id":"319d7413-4b18-48bb-8e49-755534fdd081","year":2022},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:43.951188Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:92db50aca39ae2c2d06bcfa39da7208d034302c38af9d0dc0c340fb8b70c4466","observation_id":"e22e948d-36f2-473c-9591-49afc8fa1432","resolution":{"observed_at":"2026-08-07T14:04:48.825002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:48.604754Z","title":"Feature contrast difference and enhanced network for RGB-D indoor scene classification in internet of things,","venue":null,"work_id":"d9d2d109-11e2-4878-89e4-cc81ebacf809","year":2025},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:44.055735Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:d62400152b784380d9c1cabfb994d7aedbf12f02e5552563a5a5f08fba4571bf","observation_id":"778c6bfb-d801-4078-b914-f7a8c686ab4b","resolution":{"observed_at":"2026-08-07T14:04:48.667641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:48.470126Z","title":"Continuous pseudo-label rectified domain adaptive semantic segmentation with implicit neural representations,","venue":null,"work_id":"b763d677-e337-471e-9df4-69f61a92c21c","year":2023},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:44.124837Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:bc97e89997697cca5e0d0e32bb9de640d71ee4fb5b0ce40836855e985b1642f1","observation_id":"dd5fa2c3-6626-42af-bd56-8b0dd5312197","resolution":{"observed_at":"2026-08-07T14:04:48.543041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:48.253230Z","title":"Complementary random masking for rgb-thermal seman- tic segmentation,","venue":null,"work_id":"75923d56-bab6-4027-8124-d6d2bb3c2a4d","year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:44.228530Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:39c409c6e0b01b2cacd8eb327eb7a916248e6040475569d9a9ff259e01af9272","observation_id":"8e599e41-4e86-47ce-8460-f7c1863ed1aa","resolution":{"observed_at":"2026-08-07T14:04:48.365156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:48.090811Z","title":"Self-supervised model adaptation for multimodal semantic segmentation,","venue":null,"work_id":"fc15b317-6cc4-4357-aa35-c2a7a8832a1d","year":2020},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:44.352151Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:c2e3060dda7dd800848e286f3eaefd9323085acef003fc457cb42950ef388162","observation_id":"f3d8c6cf-b583-40ca-ab48-2dc39da94976","resolution":{"observed_at":"2026-08-07T14:04:48.158206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:47.972036Z","title":"Active boundary loss for semantic segmentation,","venue":null,"work_id":"eff16052-028f-435c-8c4e-232d05eddb8e","year":2022},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:44.429525Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:bfceb978297b0946191093ee0d6d3ac78a2d4d8f6bec432e03f3f89e90ee7c9d","observation_id":"33d640e7-e37b-4e8e-a2c7-1c3b40fabbc5","resolution":{"observed_at":"2026-08-07T14:04:48.030390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:47.816583Z","title":"Conditional boundary loss for semantic segmentation,","venue":null,"work_id":"5e0fd503-71dc-423b-9ad6-7d3c5c9e73cd","year":2023},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:44.497328Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:ce0eced5579dd4e7c2d2e96b642e2329b27877ec803e67c2546734951f708ea9","observation_id":"06f0c81b-3c40-455e-b56f-8042eba7a096","resolution":{"observed_at":"2026-08-07T14:04:47.874540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:47.632984Z","title":"Guided contrastive boundary learning for semantic segmentation,","venue":null,"work_id":"07e74944-c30e-43c0-8e3e-c737775c4661","year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:44.562705Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:36a71e8b3f2a46628a5f3573780c60927289a19b6076b3fc942373f61eb587da","observation_id":"ed44b668-998b-4c86-8063-458d0e24a3e6","resolution":{"observed_at":"2026-08-07T14:04:47.720815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:47.455854Z","title":"Effective whole-body pose estimation with two-stages distillation,","venue":null,"work_id":"fd780455-88e2-40d7-b835-bfcb47986ae7","year":2023},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:44.645852Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:42f04be2a378fe88309efb101d2541845012819229b4291ab4f42d958aa4d1f3","observation_id":"2f6fa6eb-9ef7-4bfb-94b9-3dda2982ffb3","resolution":{"observed_at":"2026-08-07T14:04:47.548686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:47.281158Z","title":"Augmented reality meets computer vision: Efficient data generation for urban driving scenes,","venue":null,"work_id":"c1a02ebf-8d6c-46bf-9452-765616a58275","year":2018},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:44.722024Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:1636f7d1940bd65f2fe7b9c480efe9c0cf8bcff842e4fede2c8ccd647a5b3897","observation_id":"2c56e93e-aa87-4463-a204-e6d79c0cefcc","resolution":{"observed_at":"2026-08-07T14:04:47.367359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:44.814674Z","title":"Playing to Vision Foundation Model’s Strengths in Stereo Matching,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:44.814674Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:880ed3fab56ed183a7b6b625e892e466100e730506190db2da009e6f53c8a8be","observation_id":"3bc236b0-dbb1-4ae7-9f64-b157ee0aadd8","resolution":{"observed_at":"2026-08-07T14:04:44.814674Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-07T14:04:47.128567Z","title":"Decoupled Weight Decay Regularization,","venue":null,"work_id":"8f19569a-17a9-4056-a075-fb7493628260","year":2019},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:44.884642Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:d79e67236fa7478643244c27a15f33cb9e9971e5f0778d13cd58b105691e99d6","observation_id":"eb0619e9-1880-4792-9c97-0afb7ed04a17","resolution":{"observed_at":"2026-08-07T14:04:47.202784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:47.007977Z","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,","venue":null,"work_id":"5d71cb92-ff0f-4564-b5a6-e5a7af6f7252","year":2017},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:44.962378Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:77f93a16e0b5c26ab67b9a3a09c2a07e17a10355d562022c50ba99ec2123d74b","observation_id":"811d938e-19a6-4252-99d9-8755ed8fc6c2","resolution":{"observed_at":"2026-08-07T14:04:47.056931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:46.891686Z","title":"Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation,","venue":null,"work_id":"234867e2-7477-4ab4-a2c8-9c160ffec2eb","year":2019},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:45.082842Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:8366835b86e38d5fec7f2aa63b8a5509acea781e70563c3d6a1b9a734734574d","observation_id":"5d98b2a6-e041-4859-8e41-76d2f36a09a4","resolution":{"observed_at":"2026-08-07T14:04:46.946658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:46.745106Z","title":"CMX: Cross-modal fusion for RGB-X semantic segmentation with Transformers,","venue":null,"work_id":"d328e6d1-9ff6-4269-9452-8eb1746d2c6a","year":2023},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:45.161914Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:c2ef8823ed7a1b5c1f192d98b1f67c19643027e27c134044ac360a077d5a8ade","observation_id":"48e34cfc-a73e-4ae5-ab2f-f38684171833","resolution":{"observed_at":"2026-08-07T14:04:46.820896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.04256","last_updated":"2025-09-10T06:02:46Z","snapshot_observed_at":"2026-08-09T08:02:52.921263Z","submitted_at":"2024-04-05T17:59:44Z","title":"Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.04256","snapshot_observed_at":"2026-08-07T14:04:45.222798Z","title":"Sigma: Siamese mamba network for multi-modal semantic segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:45.222798Z"},"links":{"cited_paper":"/paper/2404.04256","citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:f451652d2af6903cf6359fc7dc107263e49f6003e7dc62ed4817a061246d0c65","observation_id":"8a714b12-82fd-445c-b1b3-0d4f5267d774","resolution":{"observed_at":"2026-08-07T14:04:45.222798Z","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":"2026-08-07T14:04:46.596161Z","title":"Geminifusion: Efficient pixel-wise multimodal fusion for vision transformer,","venue":null,"work_id":"3e267b1f-e848-4a8a-994e-5c0b3d6e411f","year":2024},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:45.307679Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:cfda0987d1d677eb17fc8a4559a05c2786013166d9f4823212a9effcf9f1f85e","observation_id":"d1a985bb-9f0b-4e08-9660-17ca079194cf","resolution":{"observed_at":"2026-08-07T14:04:46.671765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:46.480847Z","title":"Sun RGB-D: A RGB-D scene understanding benchmark suite,","venue":null,"work_id":"3c703258-ff49-4a4c-a635-8efdeb1255d2","year":2015},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:45.383519Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:e9e71dde02413d7cee9613407540f84b81c111b2df02ee6cebf2044613777514","observation_id":"df0a1120-b92a-4851-8243-290782af4559","resolution":{"observed_at":"2026-08-07T14:04:46.535382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:46.352374Z","title":"Improving semantic segmentation via video propagation and label relaxation,","venue":null,"work_id":"e277fc3c-d376-4e92-a96a-159a9261d607","year":2019},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:45.451934Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:59387ceb3847ece91862c3c06154aa6ff99ae959d683f1976b2751a3d1d5a3d9","observation_id":"f3246946-3ce4-4c26-8e6c-c2737076fef9","resolution":{"observed_at":"2026-08-07T14:04:46.406355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:46.224679Z","title":"Multi-target pan-class intrinsic relevance driven model for improving semantic segmentation in autonomous driving,","venue":null,"work_id":"01cba58a-4b07-4f29-bc72-fccb44811362","year":2021},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:45.525649Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:9cefb61ba0f29e0a21167e30d716ec2946a7021de0a2e26290b51caadd0a54bc","observation_id":"68082ad9-50ab-4429-aa9c-169a2d1bac4d","resolution":{"observed_at":"2026-08-07T14:04:46.293816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T14:04:46.086109Z","title":"Warp-refine propagation: Semi-supervised auto- labeling via cycle-consistency,","venue":null,"work_id":"2bc05655-621c-4440-b8d6-cde0391a1d3e","year":2021},"citing_paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T14:04:45.598407Z"},"links":{"citing_paper":"/paper/2505.20041"},"observation_digest":"sha256:4c12b10ee2f887b4da4153157c466877e3da3589fb5ae4217e6d8a00e918d1c1","observation_id":"8f207aa7-6539-4309-a369-ac6de3bced6e","resolution":{"observed_at":"2026-08-07T14:04:46.131198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.20041","last_updated":"2025-05-26T14:26:31Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T03:36:27.073441Z","submitted_at":"2025-05-26T14:26:31Z","title":"DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":3,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":1,"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2505.20041."}