{"as_of":"2026-08-16T05:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8092ad1767bdc302bcd514676cfd8985931997d17f4a329100bd3563b077672c","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":"2026-08-15T06:32:42.880941+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":"sha256:9a63605a277e8588e7da64edcb058ebfffa46c78f5eab84e3014254bed0e16f5","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"sha256:080f0d6077d91f3f7a4cacca00f5ecf67ff4868adcb21e21bdaac70b9ce6af13","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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:a94910218f576038a4fafa89dd51deea2fc3fe5ef332477a0f675773dfd66918","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.10326","last_updated":"2021-09-23T10:23:51Z","snapshot_observed_at":"2026-08-14T16:02:58.912669Z","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:f705125a59802fd1506999fb264659bb54056426d2293e891b8921f49cd9d01b","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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:e15082e6cc1f47944722fab845329f29ff32c0173e4ab413d19436348e5ec26b","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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:aa198ad86c6720ea92c6beedf0096e15f15d0a5acb1b1266526bbb537ffe24c6","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.14211","last_updated":"2022-03-27T05:03:56Z","snapshot_observed_at":"2026-08-14T00:18:19.852451Z","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":"sha256:742e3c7b9bcdb190d48315b892b788cbe8c1e7d9e98a8aa200520953f8397956","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"sha256:75dfd76fbce33febbcebb89fbbee0a9f0e7639849984a1625fb3108d34f498dd","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"sha256:7ec66b6052f8b16580dc27fa1b35db42e34f6c080437b3ed9d6840ed9078eda4","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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:bcf9b82a933de84df7f42555f01c5526a4071b51d34eea73a5b2e9ccb477070c","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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:a17a3b97d27ec73947f7939f63929bc163c70c690eb0c9be39b0e5a1777d9644","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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:b1b195e9da5195e362dddcdd8b23768afd62ac6f0a3f05508c11a98449df3b09","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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:e38ad11324665b2d569bdb4c0b9d25cfdaa179374e14976d92dc17244fd0958b","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"sha256:1c9c09cd51a8acbecd64a147fc59f1da5833c88e8763c5ee6a598eb7b1c403bf","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"sha256:1bdfdc2dc8c661e75ddcd17a8ecadafac5e13c8abb703a85c1bb90a237b2c9a1","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"sha256:6c20e5dde38d41ce0a80c69ad501e444961dfab788691b29802d4ef1a7790652","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"sha256:501b01f70592dd19db30598da14693e4cc1ae996f8f8a89c4a7317d2f83feb6a","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"sha256:1d28223b25bced4caf6cb83de5c2b4bf95c9401ec20ecb622a97565d19c05741","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 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2605.10251."}