{"as_of":"2026-08-14T13:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e6258f2af0d1b7652b573847990fdda238723b1d2c51189220287c7f7e35e333","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:31:29.642442Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2505.24361/citation-record","integrity":"/paper/2505.24361/integrity","json":"/paper/2505.24361/citation-record.json","paper":"/paper/2505.24361"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:31:34.421879Z","title":"The modality focusing hypothesis: Towards under- standing crossmodal knowledge distillation,","venue":null,"work_id":"769f10d8-007e-42f0-9037-91682c474fdb","year":2022},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:25.808728Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:a13077e702fe9cbdbb6c51c3e0b4e97f46a9f1d90207422b99dd1f446607292f","observation_id":"28ad3105-aecf-4f90-8e41-c0cf752358d7","resolution":{"observed_at":"2026-08-07T12:31:34.560912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:34.231674Z","title":"Model compression,","venue":null,"work_id":"f95cf2c6-4fad-4024-ad95-d58d5df11177","year":2006},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:25.875432Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:c751d7052290df32593460cd21ed8d6796372436d1699d4a410acad07f3e6bf7","observation_id":"dab23807-88af-4fcd-9833-3391cf2875cb","resolution":{"observed_at":"2026-08-07T12:31:34.326750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-07T12:31:26.002567Z","title":"Distilling the knowledge in a neural network,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:26.002567Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:fbc17b9edecef0480f18a2c3b83b4bb91d4baf5a2f905d53e8cdd2e9ad23b7db","observation_id":"60401841-2dfe-4694-8c2c-b157743d8768","resolution":{"observed_at":"2026-08-07T12:31:26.002567Z","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":"2026-08-07T12:31:33.979837Z","title":"Multi-level logit distillation,","venue":null,"work_id":"5ff79903-277d-41d3-b9c7-8fa281828ccc","year":2023},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:26.125199Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:c8dea9b4641d71dec650ef4c971d5519167bce2361556d0214bd70021d61c3a4","observation_id":"30b0b901-11e5-4297-b000-cfebfc54f9d2","resolution":{"observed_at":"2026-08-07T12:31:34.090259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:33.885850Z","title":"Class attention transfer based knowledge distillation,","venue":null,"work_id":"9c6c7765-eff8-4da5-b7bd-77489cb4f04a","year":2023},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:26.225620Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:0b96613a04a8159a9f3b23860739a312177d6f9e158b6a3adefa81cf7ca3c1b1","observation_id":"52888dcc-47f7-4c04-adc8-e611ac520f47","resolution":{"observed_at":"2026-08-07T12:31:33.914179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10536","last_updated":"2022-12-28T04:02:19Z","snapshot_observed_at":"2026-08-13T15:39:43.074130Z","submitted_at":"2022-05-21T08:30:58Z","title":"Knowledge Distillation from A Stronger Teacher","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10536","snapshot_observed_at":"2026-08-07T12:31:26.294762Z","title":"Knowledge distillation from a stronger teacher,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:26.294762Z"},"links":{"cited_paper":"/paper/2205.10536","citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:568c8abca4d732e666fdcc53447d438f8fcc021c03bd3b4de54b3fcf3483255e","observation_id":"9b355d41-6e31-4373-8f2e-5c770d3dca1b","resolution":{"observed_at":"2026-08-07T12:31:26.294762Z","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":"2026-08-07T12:31:33.644905Z","title":"Lite-mkd: A multi-modal knowledge distillation framework for lightweight few-shot action recognition,","venue":null,"work_id":"2908b75a-662c-4763-ac39-251a3bcb9a2b","year":2023},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:26.474007Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:baf48596596ffaea9d010db3ccec537fe061ee230c75f46516e64836429e143c","observation_id":"80993ce9-590d-4189-b628-09b79a223fae","resolution":{"observed_at":"2026-08-07T12:31:33.764816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:33.395715Z","title":"Cross-modal distillation for rgb- depth person re-identification,","venue":null,"work_id":"2cde7876-149d-4f67-bfd6-372ac435e509","year":2018},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:26.668648Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:1f5dff80c92813b18ed3b37b04ec4dbfeff6bb12fc9a3c7e3ebd40863cc1774a","observation_id":"c3d3cfaa-bb36-4ed8-be13-e415f11fda0b","resolution":{"observed_at":"2026-08-07T12:31:33.528940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09564","last_updated":"2021-06-17T14:46:57Z","snapshot_observed_at":"2026-07-06T11:20:23.028632Z","submitted_at":"2021-06-17T14:46:57Z","title":"Knowledge distillation from multi-modal to mono-modal segmentation networks","version":1},"cited_work":{"arxiv_id":"2106.09564","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.09564","snapshot_observed_at":"2026-08-07T12:31:30.156033Z","title":"Knowledge distillation from multi-modal to mono-modal segmentation networks","venue":"cs.CV","work_id":"ad76077b-3b7e-460d-ac7d-5d92e3708efb","year":2021},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:26.843805Z"},"links":{"cited_paper":"/paper/2106.09564","citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:a233211a2c0c66a4d5ae63c393493eaa78d89630fe2b953a05d41f342394ce21","observation_id":"fada5fb2-c376-4562-8b28-639d262f0132","resolution":{"observed_at":"2026-08-07T12:31:30.232050Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:33.342770Z","title":"Acnet: Attention based network to exploit comple- mentary features for rgbd semantic segmentation,","venue":null,"work_id":"78571b71-5109-4ae6-930e-4247a00fdc88","year":2019},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:26.959477Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:2b0cbb19e66d869ad64ae339bcb1c13b25e72ca583fea2998bdc08d350ff915e","observation_id":"51822a35-7854-4ed1-82fc-bc3e6497b26a","resolution":{"observed_at":"2026-08-07T12:31:33.383951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.04484","last_updated":"2025-03-06T14:06:24Z","snapshot_observed_at":"2026-08-13T05:07:47.187275Z","submitted_at":"2023-12-07T17:59:53Z","title":"FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.04484","snapshot_observed_at":"2026-08-07T12:31:27.052264Z","title":"Frnet: Frustum-range networks for scalable lidar segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:27.052264Z"},"links":{"cited_paper":"/paper/2312.04484","citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:773a181a663eab725ae497c21384423d273c4957ad6136b0f3bc01da86baffcb","observation_id":"02a4db32-b6ae-430e-965a-69e19d46d0e9","resolution":{"observed_at":"2026-08-07T12:31:27.052264Z","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":"2026-08-07T12:31:33.225279Z","title":"Fusenet: Incorporating depth into se- mantic segmentation via fusion-based cnn architecture,","venue":null,"work_id":"12f5c3f9-289e-4589-a771-d02d6680e203","year":2016},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:27.134692Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:857f9999be16f97d58deb1dd13324c3510ff381c5e5f14dd033bd449812ea134","observation_id":"654f9d07-b0e3-47ca-ad07-5a0696b50bde","resolution":{"observed_at":"2026-08-07T12:31:33.271916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:33.113074Z","title":"Indoor semantic segmentation using depth information,","venue":null,"work_id":"39d5f089-ee56-485a-b45f-f2e7956e15b0","year":2013},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:27.227075Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:72a959aaa6665a29f7df778b68a7ff2d343c11fefd7012e6b456a60847001956","observation_id":"7cdea9bc-b8f2-44d2-b0d8-2c9360ff56c2","resolution":{"observed_at":"2026-08-07T12:31:33.159651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:32.997712Z","title":"Pixel difference convolutional network for rgb-d semantic segmentation,","venue":null,"work_id":"5bfed294-013a-43f2-8ad9-bd797e5bf9e4","year":2023},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:27.333691Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:f3d89467591054258cf0e2253d9b5fd2b134f2105af1181fb89c3e7c338c1b2d","observation_id":"0b9a7962-18ab-439d-b711-0d672488622c","resolution":{"observed_at":"2026-08-07T12:31:33.045394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:32.861452Z","title":"Rdfnet: Rgb-d multi-level residual feature fusion for indoor semantic segmentation,","venue":null,"work_id":"0c54e195-9716-4174-8dfa-57bf9a54b938","year":2017},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:27.443021Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:43409dea33632a423aa4b4f70c9cd6ce0f76d762c968fec0209f3864c0a408f2","observation_id":"308946b6-3f30-409c-bb91-5f72d1923387","resolution":{"observed_at":"2026-08-07T12:31:32.922958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.01054","last_updated":"2018-08-06T17:11:25Z","snapshot_observed_at":"2026-08-13T05:07:33.509835Z","submitted_at":"2018-06-04T11:33:57Z","title":"RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.01054","snapshot_observed_at":"2026-08-07T12:31:27.532748Z","title":"Rednet: Residual encoder-decoder network for indoor rgb-d semantic segmentation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:27.532748Z"},"links":{"cited_paper":"/paper/1806.01054","citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:03fe9e1ac9f76d86f4eb548f0456fb59cad8b205129537b27d33f2c9abf2d711","observation_id":"638a579c-0351-47d4-8e6e-96e6abadf44c","resolution":{"observed_at":"2026-08-07T12:31:27.532748Z","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":"2026-08-07T12:31:32.740920Z","title":"Context-aware interaction network for rgb-t semantic segmenta- tion,","venue":null,"work_id":"937944ed-c4cd-44db-a893-37a11c5d2508","year":2024},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:27.679142Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:df8c7235202ae0fe6fb5f1b4ef9e05c8b6b82b6b59ef69d1596393f3ec99870f","observation_id":"48e0426b-0c37-4842-bc87-0374c1dbaa3f","resolution":{"observed_at":"2026-08-07T12:31:32.787670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:32.602561Z","title":"Fuseseg: Semantic segmentation of urban scenes based on rgb and thermal data fusion,","venue":null,"work_id":"52db5d1a-b3ae-4de2-a207-73bac919d687","year":2021},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:27.802802Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:cb5eb2ba008e98587e43cdba454cfa0934794c42670837744930c07e1f674fba","observation_id":"5b288159-f955-4079-bbc9-7dd3df533ac2","resolution":{"observed_at":"2026-08-07T12:31:32.669029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:32.451191Z","title":"Masked generative distillation,","venue":null,"work_id":"efa41dc4-5c32-4a0b-b445-51daaa1956ba","year":2022},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:27.940355Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:fe272575191aae3303cb6d397b64b2f955a8b25225ef421cc01afdc776eb99f3","observation_id":"8c5984b6-0acb-4a98-9adc-3dc460091932","resolution":{"observed_at":"2026-08-07T12:31:32.503295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:32.331701Z","title":"Prototype knowledge distillation for medical segmentation with missing modality,","venue":null,"work_id":"e882003f-67a0-4447-8556-7b241c443c77","year":2023},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.039356Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:29dfa87aede172d381b09de3c851bca5950e66528b39a6aa84aae2041cac8c8a","observation_id":"7d504c3b-2bd6-4f5d-aa93-087eb9197d71","resolution":{"observed_at":"2026-08-07T12:31:32.389175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:32.211921Z","title":"Rethinking knowledge distillation with raw features for semantic segmentation,","venue":null,"work_id":"d1a5286c-9a66-4f92-95d4-32cdd870d379","year":2024},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.109239Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:cc25a04697b40dcbd27b1bdc7ad61f934712d3309b61647fdcecd631298bee19","observation_id":"90513cae-b5a0-4d44-9cc5-db87e7e739d9","resolution":{"observed_at":"2026-08-07T12:31:32.273602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:32.076949Z","title":"Disentangled representation learn- ing,","venue":null,"work_id":"b951f8a3-43de-450a-a19e-0b3b0ce640d7","year":2024},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.217597Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:a82eac7e3ca4f3e25e4aa7be635a486a4ea924e14f6982ef2b5fc0457a2cde8e","observation_id":"8d349e99-7024-4ba9-bb6c-02e8f25a27a3","resolution":{"observed_at":"2026-08-07T12:31:32.135811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.06176","last_updated":"2019-05-14T14:16:40Z","snapshot_observed_at":"2026-07-06T06:45:08.082087Z","submitted_at":"2018-06-16T03:48:50Z","title":"Learning Factorized Multimodal Representations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.06176","snapshot_observed_at":"2026-08-07T12:31:28.349593Z","title":"Learning factor- ized multimodal representations,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.349593Z"},"links":{"cited_paper":"/paper/1806.06176","citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:9641e37bcf1888231f4f1064e435e381345d447b8a58f4de16a1ba020d1a2ad8","observation_id":"955bc553-e181-4244-b73d-921197687a27","resolution":{"observed_at":"2026-08-07T12:31:28.349593Z","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":"2026-08-07T12:31:31.937890Z","title":"Learning disentangled representation for multimodal cross-domain sentiment analysis,","venue":null,"work_id":"032cfd53-8384-40d1-a1df-9ea712c74494","year":2023},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.456121Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:b4504a6fc6020a038a6f415084503e93555f8aee934d94b2666597214da028f8","observation_id":"a0020ace-46e4-4b80-977f-356a69989dd0","resolution":{"observed_at":"2026-08-07T12:31:31.995085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:31.808266Z","title":"Pre- dict, prevent, and evaluate: Disentangled text-driven image manipulation empowered by pre- trained vision-language model,","venue":null,"work_id":"8d39085f-e9ac-4123-9d0a-534e8fe42c3e","year":2021},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.560422Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:9e66b2d55f076d400ab0309ccbc4e9cb44cde6570e640a61958aef8ac1ce49cf","observation_id":"46a7fdb0-2468-448d-bb07-2f4966d2648f","resolution":{"observed_at":"2026-08-07T12:31:31.861278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:31.667051Z","title":"Towards counterfactual image manipulation via clip,","venue":null,"work_id":"46c30b84-7739-40bb-9e48-941c47094998","year":2022},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.645656Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:435210cecf5b7ab5efb427e2c5124de6bba23a9d9224c0703e7bd6fe2f12be13","observation_id":"da19ff45-ba54-47df-a9ff-708c6f716331","resolution":{"observed_at":"2026-08-07T12:31:31.735299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:31.513226Z","title":"Disentangling visual and written concepts in clip,","venue":null,"work_id":"9e07ab2c-37a2-43c1-bdbc-e78775bb85bf","year":2022},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.680638Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:5cfff8923b31075c06da8ca55490d0f5e9aabd3959109fba08be318d2cc6d72d","observation_id":"765a8b2a-8533-4643-aea3-4a8e8e15a291","resolution":{"observed_at":"2026-08-07T12:31:31.590233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:31.395909Z","title":"Discom-kd: Cross-modal knowledge distillation via disentangle- ment representation and adversarial learning,","venue":null,"work_id":"f867a3c8-7c39-4ed3-86c6-7626e5798154","year":2024},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.738455Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:8f6e77ed7277ebec41ffe22a314f0e21e0652e234551e2f148efa64225c15c37","observation_id":"38515c32-1ca0-4c72-bf31-b9f0e59d1531","resolution":{"observed_at":"2026-08-07T12:31:31.464408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.09412","last_updated":"2018-04-27T21:39:25Z","snapshot_observed_at":"2026-08-08T10:28:19.597631Z","submitted_at":"2017-10-25T18:30:49Z","title":"mixup: Beyond Empirical Risk Minimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.09412","snapshot_observed_at":"2026-08-07T12:31:28.814544Z","title":"mixup: Beyond empirical risk mini- mization,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.814544Z"},"links":{"cited_paper":"/paper/1710.09412","citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:6f642d22246af2171743a730f9414f486a950cd1adcd4063ac2ba64fe6b0d659","observation_id":"23799868-5340-472b-b0b2-133a6253d5a4","resolution":{"observed_at":"2026-08-07T12:31:28.814544Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-07T12:31:28.896627Z","title":"Representation learning with contrastive predictive coding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.896627Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:edbacacf6e2ec4f5b8460acd6b4f1478b39d263d64f5c3a72d174796b55cface","observation_id":"0ed195c9-e415-43ee-afcb-0e3b0e9f2a9e","resolution":{"observed_at":"2026-08-07T12:31:28.896627Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.04256","last_updated":"2025-09-10T06:02:46Z","snapshot_observed_at":"2026-08-13T16:49:45.612160Z","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":"2026-08-07T12:31:28.947196Z","title":"Sigma: Siamese mamba network for multi-modal semantic segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.947196Z"},"links":{"cited_paper":"/paper/2404.04256","citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:9f18f3c4564dbafcd502fbfed5d85526e823a9def6ded49be4e20ebd6a4ad7da","observation_id":"bf746263-8887-4674-902d-c153eb433929","resolution":{"observed_at":"2026-08-07T12:31:28.947196Z","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":"2026-08-07T12:31:31.209904Z","title":"Indoor segmentation and support inference from rgbd images,","venue":null,"work_id":"31defa9b-3199-4526-9e60-b46b4e8d9705","year":2012},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:28.989927Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:abedc449373ef3731f21fd5b4830d512a5554aa9bb2a8e44f578d77857fc3c8c","observation_id":"53d76935-0af7-42b9-95d4-56993fa265a7","resolution":{"observed_at":"2026-08-07T12:31:31.297336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:31.025538Z","title":"Omnivore: A single model for many visual modalities,","venue":null,"work_id":"52f07ace-3d15-4e13-ac3e-f7494e5b61f0","year":2022},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:29.095939Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:edf9e85a5c00941c24f5cd3c81b34d0ede7459b655dd0a563132ae6de05a57f4","observation_id":"0c78ff97-bfb7-4909-8c57-621f2c1050fe","resolution":{"observed_at":"2026-08-07T12:31:31.106455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:30.845132Z","title":"Urban modelling and semantic labelling benchmark,","venue":null,"work_id":"25f571eb-fe3d-4df7-9761-8710936edcc1","year":2024},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:29.182392Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:14c12d87fa9ae9c689b5e5d90c78cab649828e0112db20040b1fcb64bcc4933a","observation_id":"b671c621-a0fa-4dce-8013-4c8282626c0b","resolution":{"observed_at":"2026-08-07T12:31:30.932704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.03388","last_updated":"2023-07-07T04:58:34Z","snapshot_observed_at":"2026-08-13T11:00:29.749027Z","submitted_at":"2023-07-07T04:58:34Z","title":"General-Purpose Multimodal Transformer meets Remote Sensing Semantic Segmentation","version":1},"cited_work":{"arxiv_id":"2307.03388","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.03388","snapshot_observed_at":"2026-08-07T12:31:29.831277Z","title":"General-Purpose Multimodal Transformer meets Remote Sensing Semantic Segmentation","venue":"cs.CV","work_id":"84e6e3cc-a13d-43a1-b471-baceca917fba","year":2023},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:29.270213Z"},"links":{"cited_paper":"/paper/2307.03388","citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:bf0d2640412f2f4aae69a5efe5d9cd8c766187947ec4e02391f9871703a5e388","observation_id":"656553fe-0837-4104-a47b-9aa27ac420b1","resolution":{"observed_at":"2026-08-07T12:31:29.874541Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:30.703660Z","title":"Mid-air: A multi-modal dataset for extremely low altitude drone flights,","venue":null,"work_id":"ef31ec5f-3024-4de1-be43-ea83060fc8f0","year":2019},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:29.393537Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:0d7a2b79481d763a8432cf8331a186ec267985a6a96051cfd45b648289a52111","observation_id":"06c4035f-a4e2-4442-8669-e292541e6da4","resolution":{"observed_at":"2026-08-07T12:31:30.767609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.03643","last_updated":"2016-02-26T02:21:52Z","snapshot_observed_at":"2026-07-06T04:36:10.443201Z","submitted_at":"2015-11-11T20:27:54Z","title":"Unifying distillation and privileged information","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.03643","snapshot_observed_at":"2026-08-07T12:31:29.476866Z","title":"Unifying distillation and privileged information,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:29.476866Z"},"links":{"cited_paper":"/paper/1511.03643","citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:c4203bee43c1d4600c4b24f53d71a46fdd8e01e3cbfb39eff17f01931cb8f9ea","observation_id":"904d8e21-0302-4aa9-958f-dd891b37e95f","resolution":{"observed_at":"2026-08-07T12:31:29.476866Z","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":"2026-08-07T12:31:30.548523Z","title":"Encoder-decoder with atrous separable convolution for semantic image segmentation,","venue":null,"work_id":"62cafabe-15ac-4235-9699-afa922ce1b3d","year":2018},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:29.568763Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:5f8985d32944ac707c3585e5673ea1bd09915effe3dba8e8051ff7ee6256e18f","observation_id":"ff867cd8-97d9-41e4-be89-ad6302efed69","resolution":{"observed_at":"2026-08-07T12:31:30.609341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-07T12:31:30.398783Z","title":"The modality focusing hypothesis: Towards under- standing crossmodal knowledge distillation,","venue":null,"work_id":"4da7fa14-5e84-4074-a8f2-4336563262de","year":2023},"citing_paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T12:31:29.642442Z"},"links":{"citing_paper":"/paper/2505.24361"},"observation_digest":"sha256:7628e4d58544d61593559d1ad5370b68dce9b60193da570535ba6a3b009b2cfa","observation_id":"3bae90ae-14c9-47f1-b187-d6ef6a55b2ba","resolution":{"observed_at":"2026-08-07T12:31:30.474724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.24361","last_updated":"2025-05-30T08:53:35Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T03:21:55.954434Z","submitted_at":"2025-05-30T08:53:35Z","title":"Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":2,"verified_fuzzy":28},"total_outbound_references":39},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.24361."}