{"as_of":"2026-08-09T16:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b464b14db358d6fce3013b0de5b9ec86a16918f3d5f55a114dee23535779b2b1","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-12T03:20:11.827500Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"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/2605.10251/citation-record","integrity":"/paper/2605.10251/integrity","json":"/paper/2605.10251/citation-record.json","paper":"/paper/2605.10251"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Depth map prediction from a single image using a multi-scale deep network","venue":null,"work_id":"d28eea21-f369-4a18-942c-b638457e1927","year":2014},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:68da8363d5b010129923cf5ad73234d6bdef5a5bc0aa71b2a650ec6eecd6e187","observation_id":"8279ba47-b849-4f49-893b-cedfeab7eaf8","resolution":{"observed_at":"2026-05-12T20:11:48.619593Z","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":"2026-06-05T21:23:00.469572Z","title":"Deeper depth prediction with fully convolutional residual networks","venue":null,"work_id":"395e5b69-5813-4fc8-bb3c-09ed7d11151b","year":2016},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:df54c17ef876daaf6ba0cbf1929d908be1fa6161dc63a98d93daa49368fb764c","observation_id":"ea13dd6b-e0a6-46b5-bf90-3a1824f15ba0","resolution":{"observed_at":"2026-05-12T20:11:48.631753Z","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":"2026-06-05T21:23:00.469572Z","title":"Deep ordinal regression network for monoc- ular depth estimation","venue":null,"work_id":"6a578e1a-1a42-4c51-bea2-e1e9e0ec1666","year":2018},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:afdc98f5e28c9cd5091dae192fcfe218e9ea73d8d1b12bcf346a3ceb6e8b8df4","observation_id":"14ba58ab-cb1c-4770-a586-40c2011ab904","resolution":{"observed_at":"2026-05-12T20:11:48.658353Z","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":"1907.10326","last_updated":"2021-09-23T10:23:51Z","snapshot_observed_at":"2026-08-08T09:58:54.046584Z","submitted_at":"2019-07-24T09:31:24Z","title":"From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation","version":6},"cited_work":{"arxiv_id":"1907.10326","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1907.10326","snapshot_observed_at":"2026-07-03T14:28:32.054457Z","title":"From big to small: Multi-scale local planar guidance for monocular depth estimation","venue":null,"work_id":"a87063a0-b722-4757-9cc8-0fd30352539a","year":1907},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"cited_paper":"/paper/1907.10326","citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:89f976abd31fe9f26896fd558c0abb77f2305bed1a7894802ccd0ec2d0d8a29b","observation_id":"8bbb57a9-5733-4ff7-8c97-b206e741ae0b","resolution":{"observed_at":"2026-05-12T03:21:18.760084Z","resolver_source":"arxiv_id","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":"2026-06-05T21:23:00.469572Z","title":"AdaBins: Depth estimation using adaptive bins","venue":null,"work_id":"33e31bb1-8abf-44ac-9ab1-fd0fc9cd3866","year":2021},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:4febff06da2b6161628da275868d3138974f5f3ca879e4b5c59f90e60d064fee","observation_id":"cbb2645c-5dc2-4c29-9df3-62784dc134f6","resolution":{"observed_at":"2026-05-12T20:11:48.653514Z","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":"2026-06-05T21:23:00.469572Z","title":"Vi- sion transformers for dense prediction","venue":null,"work_id":"c5354f57-ffa3-44a6-be74-dc80b15b45e8","year":2021},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:1c8f6bd993a2f5fbc345fea018d03c64f460ae8e9faaf9bd567f68d1bc45db48","observation_id":"57f136a6-0de2-4ad9-8f8c-1ff4806cbfb0","resolution":{"observed_at":"2026-05-12T20:11:48.675504Z","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":"2203.14211","last_updated":"2022-03-27T05:03:56Z","snapshot_observed_at":"2026-08-05T04:19:25.294222Z","submitted_at":"2022-03-27T05:03:56Z","title":"DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation","version":1},"cited_work":{"arxiv_id":"2203.14211","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.14211","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2203.14211 , year=","venue":null,"work_id":"5e9d5437-2ca4-47ca-9c91-421a575fb3f5","year":2022},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"cited_paper":"/paper/2203.14211","citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:5ec748c80b7c686a2f4ff5d093e6e799ee68940d2a8a6d184809314f07c4081d","observation_id":"d6dffa8e-349a-4a8c-a0d2-3f65f15b3288","resolution":{"observed_at":"2026-05-12T03:21:18.763204Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2026-06-05T21:23:00.469572Z","title":"Graph- based context reasoning for scene understanding","venue":null,"work_id":"dce875ba-54a5-4afa-8f43-1718b814fb93","year":2020},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:6b5d4039976fc3613df22b72f34e32c8d9a59a2b027d9700a30fc409dd9aceaa","observation_id":"3c088a3f-aa2b-437b-ae6c-0dbe79e3c348","resolution":{"observed_at":"2026-05-12T20:11:48.635976Z","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":"2026-06-05T21:23:00.469572Z","title":"Induc- tive representation learning on large graphs","venue":null,"work_id":"591ec277-bbae-485e-8078-7bd760d7de95","year":2017},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:29aea2efce09608c5de88846b0491a6df07e3558e407d686291b2e83d74a834f","observation_id":"c4dc5782-2b45-4237-8b5c-8023d3d6abfe","resolution":{"observed_at":"2026-05-12T20:11:48.640455Z","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":"2026-06-05T21:23:00.469572Z","title":"Indoor segmentation and support inference from RGBD images","venue":null,"work_id":"c95456a7-7b5d-4b70-8cfe-4429c0153532","year":2012},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:847df888844debb23d99a99dd0945bcc034a858881b4a085e2e158c3c3b86f3f","observation_id":"9da3d90a-c580-4dba-be84-94b35ae0b639","resolution":{"observed_at":"2026-05-12T20:11:48.624074Z","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":"2026-06-05T21:23:00.469572Z","title":"WHU: A large- scale dataset for stereo depth estimation in aerial scenarios","venue":null,"work_id":"2bedb0d6-5e63-4525-8ae3-6d537384fe5e","year":2022},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:0f2fec63371ad6f3409777d914058bc1bc034eb1cf29eaf164504dcf521aa66f","observation_id":"f5d5d50d-15bf-42ce-ae61-b88d0653b96c","resolution":{"observed_at":"2026-05-12T20:11:48.644696Z","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":"2026-06-05T21:23:00.469572Z","title":"A multi-view stereo bench- mark with high-resolution images and multi-camera videos","venue":null,"work_id":"37b779db-9b68-4ec4-a7b1-24019b0b9b97","year":2017},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:6917736aede2ba8ebff011e3830d7e580d7abef196655821d31ec0e63439f061","observation_id":"3dc9f400-92dd-4e7e-ae26-021eb8a8732c","resolution":{"observed_at":"2026-05-12T20:11:48.649164Z","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":"2026-06-05T21:23:00.469572Z","title":"Mid-Air: A multi-modal dataset for ex- tremely low altitude drone flights","venue":null,"work_id":"ab7a66c2-e6cb-42a0-9d65-2019515c009c","year":2019},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:8ffab3f21e2d0ade60886d1c9f598f54e0b50b6fc0e6f8da47b6c3319153ed43","observation_id":"377855f9-06f7-45dc-a562-bdfb9e7ba5b0","resolution":{"observed_at":"2026-05-12T20:11:48.680146Z","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":"2026-06-05T21:23:00.469572Z","title":"U-Net: Convolutional networks for biomedical image seg- mentation","venue":null,"work_id":"f9f8fb4c-229e-4390-99b8-2f26479306b5","year":2015},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:9ad4f2ae2dccaf8d3fee03b9412edbda20c6b2b4e2290f8fe5e2ade14d4aa6b9","observation_id":"ff82dfde-1bb2-4214-a9db-4e655e0b8bd9","resolution":{"observed_at":"2026-05-12T20:11:48.666840Z","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":"2026-06-05T21:23:00.469572Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"02f322ce-1cac-47aa-bed6-a5d9d7318510","year":2016},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:024c1ca7cdee1f7d915b0607c5dcc4bf6c8eaf6742dd1cfcb56f85affd69237f","observation_id":"8c8ee4ed-78ca-4756-b776-5ede1c9d2c30","resolution":{"observed_at":"2026-05-12T20:11:48.611861Z","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":"2026-06-05T21:23:00.469572Z","title":"Squeeze-and-excitation networks","venue":null,"work_id":"97ff1ccc-fd50-4737-b420-d11d1e3da3a8","year":2018},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:f04073dd860798a226a36ab921e6ca250c5e6bc148f2f5358ed74cbbb00a28b5","observation_id":"429e7153-82bf-4983-9e1b-fef6225f6b80","resolution":{"observed_at":"2026-05-12T20:11:48.628428Z","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":"2026-06-05T21:23:00.469572Z","title":"CBAM: Convolutional block attention module","venue":null,"work_id":"9b94c0bd-741f-4c12-9a50-0061080a7ccb","year":2018},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:bf954e535ac73bf8390ab44cb24a9dbda360fc83516c677cbde4b7a99dbaf2cb","observation_id":"a1ad6006-368a-4687-b850-f6afc59608f5","resolution":{"observed_at":"2026-05-12T20:11:48.662461Z","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":"2026-06-05T21:23:00.469572Z","title":"What uncertainties do we needinBayesiandeeplearningforcomputervision?","venue":null,"work_id":"aa2ffb5d-db3a-49e0-a1c0-75b64d032146","year":2017},"citing_paper":{"arxiv_id":"2605.10251","last_updated":"2026-05-11T09:21:04Z","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-12T03:20:11.827500Z"},"links":{"citing_paper":"/paper/2605.10251"},"observation_digest":"sha256:cbb1bbc024c2c23feeb8f1f04350e15fc4e28a297741ee4c105bf6d5920dc3b4","observation_id":"2fea5fd5-de4e-4a25-ad82-790c887e93d0","resolution":{"observed_at":"2026-05-12T20:11:48.671011Z","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":"2605.10251","last_updated":"2026-05-11T09:21:04Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-02T11:31:41.736140Z","submitted_at":"2026-05-11T09:21:04Z","title":"Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":0,"verified_exact":1,"verified_fuzzy":16},"total_outbound_references":18},"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 9 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2605.10251."}