{"as_of":"2026-08-17T01:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cb65131061867d219eb7ef7e536fb3fc08adbfb1936e32c52ec949976d475743","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T15:59:20.495062Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.00448/citation-record","integrity":"/paper/1908.00448/integrity","json":"/paper/1908.00448/citation-record.json","paper":"/paper/1908.00448"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:59:21.091018Z","title":"Dense object nets: Learn- ing dense visual object descriptors by and for robotic manipulation,","venue":null,"work_id":"e0969c72-721c-4bcb-a766-a42539847477","year":2018},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.380182Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:b189dcb4aa0e9732cf20f5a423f3fcb6d7c69ea9a01c9a62fb07fb606ca09b48","observation_id":"93d6a9a6-f5e6-4c75-866b-8b3a32b69d07","resolution":{"observed_at":"2026-08-14T15:59:21.095525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:59:21.076181Z","title":"A baseline for detecting misclassiﬁed and out-of-distribution examples in neural networks,","venue":null,"work_id":"85f9191d-b4e4-4b63-884d-b33acb535270","year":2017},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.385705Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:8fb4d4ab43fa0214e6fa603feb673bbf62c9520a7f568033e73adfee41d14016","observation_id":"c87887ff-b1ac-45b2-bbac-d9b838eabd68","resolution":{"observed_at":"2026-08-14T15:59:21.080930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:59:20.390040Z","title":"A simple uniﬁed framework for detecting out-of-distribution samples and adversarial attacks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.390040Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:5480aac69d162a3fd36dff5400bf46671d0d31ec4c263c84e7763ba4411decd7","observation_id":"117f06d7-a56c-4488-9aaf-7c0992078046","resolution":{"observed_at":"2026-08-14T15:59:20.390040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.01392","last_updated":"2019-05-23T23:48:06Z","snapshot_observed_at":"2026-08-16T01:12:24.218734Z","submitted_at":"2018-10-02T17:32:07Z","title":"WAIC, but Why? Generative Ensembles for Robust Anomaly Detection","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.01392","snapshot_observed_at":"2026-08-14T15:59:20.394380Z","title":"Generative ensembles for robust anomaly detec- tion,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.394380Z"},"links":{"cited_paper":"/paper/1810.01392","citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:7eb8ce0a44aea4b98f1ba4a40a5c65b49be39fb5aeae2ef99444828f508b122a","observation_id":"8888cfbe-5e1b-4b40-91d1-4c5558471a2d","resolution":{"observed_at":"2026-08-14T15:59:20.394380Z","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":"2026-08-14T15:59:21.051093Z","title":"Towards open set deep networks,","venue":null,"work_id":"7a04dc0f-7345-43e3-89f5-6b78f55bac64","year":2016},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.399089Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:d1cbf86321c4f08731be39844e3df4c27204a2d279f134f47e387b9aa21b13ba","observation_id":"ef40b17f-38d6-40cc-bbaa-ae93539c01f4","resolution":{"observed_at":"2026-08-14T15:59:21.055387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:59:21.035581Z","title":"Safety for mobile robotic systems: A systematic mapping study from a software engineering perspective,","venue":null,"work_id":"dfccdfdf-cdb1-425d-9358-07eac73a6617","year":2019},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.403554Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:0649ffea2fdd4d56dcd66a366e0df09a91b39b3c924da81f8aee172606dac4c7","observation_id":"92eb73d1-6c7c-48fb-ae73-791371799ed0","resolution":{"observed_at":"2026-08-14T15:59:21.040928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:59:21.021244Z","title":"Mid-fusion: Octree-based object-level multi-instance dynamic slam,","venue":null,"work_id":"a2862723-2b85-4c58-80ee-0cf144c6a95c","year":2019},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.408377Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:ef5bfb0a37f4144bbff3fa53648182d7a2d8cdc7bde931fda259073da4c72e3b","observation_id":"c9ad5fb2-0846-4eab-a08f-eb39a685ed3e","resolution":{"observed_at":"2026-08-14T15:59:21.025810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:59:21.006041Z","title":"Nice: Non-linear independent components estimation,","venue":null,"work_id":"1d16e669-5f56-4600-9da1-b2f9b1b3193d","year":2015},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.412801Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:b83f3b9240fb4444d15e4f07dff72480958e4132976eed943358d367e0db058b","observation_id":"4915cac3-6a6d-45e3-8212-fa4bd12dad1b","resolution":{"observed_at":"2026-08-14T15:59:21.010826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:59:20.417342Z","title":"Density estimation using real nvp,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.417342Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:ed5b94090217830e383030a206e7421fd927b4d0b19358038e0ac8143c074e6d","observation_id":"36f6f428-22e3-4a2f-a828-5210677a7ec9","resolution":{"observed_at":"2026-08-14T15:59:20.417342Z","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":"2026-08-14T15:59:20.978302Z","title":"Glow: Generative ﬂow with invertible 1x1 convolutions,","venue":null,"work_id":"b65dced1-9204-418c-a601-28c5564152d0","year":2018},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.421562Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:68b3ac779895ee15fae0c283b6d7e1373e9f20a79501da5ba3bc2d8b2077c89d","observation_id":"5c7ae4af-6565-4f40-8a25-a55f37152e53","resolution":{"observed_at":"2026-08-14T15:59:20.983131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.03215","last_updated":"2021-09-16T10:50:36Z","snapshot_observed_at":"2026-08-16T14:41:56.656047Z","submitted_at":"2019-04-05T18:17:25Z","title":"The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.03215","snapshot_observed_at":"2026-08-14T15:59:20.425531Z","title":"The ﬁshyscapes benchmark: Measuring blind spots in semantic segmenta- tion,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.425531Z"},"links":{"cited_paper":"/paper/1904.03215","citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:6c5dc30b3bb8838183cbced06508d7cff4d82ad0fe7d5953102ce09a016ad01f","observation_id":"a7e3a22c-1cc3-48d4-be2a-0ddd80c44748","resolution":{"observed_at":"2026-08-14T15:59:20.425531Z","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":"2026-08-14T15:59:20.963516Z","title":"Fully convolutional networks for semantic segmentation,","venue":null,"work_id":"4a4b3637-3fc3-4ace-bfa5-f4d1b6fb28c7","year":2015},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.429904Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:e0e276cb75981c0e2de083e4ecb64930653a61ffb66dfa2f0c9dc5b266cb2bbe","observation_id":"99057435-f007-4694-86a9-5e8dff576472","resolution":{"observed_at":"2026-08-14T15:59:20.968515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:59:20.434324Z","title":"Learning deconvolution network for semantic segmentation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.434324Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:7bf8c9bbb1f1e0a1ee4ba10961dd3775861489701699118af967bbe0ecea7709","observation_id":"4aea1946-3b09-4679-a97a-b07bc0592d0a","resolution":{"observed_at":"2026-08-14T15:59:20.434324Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:59:20.438284Z","title":"Mask r-cnn,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.438284Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:3b77a14dd492f949f5bbc5b82fd48fb87ed7ba4632435c09e30df6edaaba0d61","observation_id":"97c8d803-e893-4f04-bfc5-d9c946ede15a","resolution":{"observed_at":"2026-08-14T15:59:20.438284Z","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":"2026-08-14T15:59:20.926125Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks,","venue":null,"work_id":"a02ab07a-6971-4f56-98a6-fc8a1b9c5781","year":2015},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.442169Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:63081300410c310aaa78adbaa6f27227f2c425bf68f5c64e7f75cbfba9b5e0b0","observation_id":"ece51f67-8af5-4796-a942-923be0c814fb","resolution":{"observed_at":"2026-08-14T15:59:20.931305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:59:20.445981Z","title":"V olumetric instance-aware semantic mapping and 3d object discovery,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.445981Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:258b312638dc3c5be19d216fe7aa19f19e57b9b7163fef92bafe2f61624ddddd","observation_id":"c482447e-b304-4402-9844-56cb7cd8639d","resolution":{"observed_at":"2026-08-14T15:59:20.445981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1505.07293","last_updated":"2015-05-27T12:54:17Z","snapshot_observed_at":"2026-08-14T22:46:56.530601Z","submitted_at":"2015-05-27T12:54:17Z","title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.07293","snapshot_observed_at":"2026-08-14T15:59:20.449790Z","title":"Segnet: A deep con- volutional encoder-decoder architecture for robust semantic pixel-wise labelling,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.449790Z"},"links":{"cited_paper":"/paper/1505.07293","citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:8c4bd92aacfa2ac6f1ae224dc3141282442fa36dbd8dacbafb7ff810bbef2a3d","observation_id":"0d3bcde5-7681-465a-a74e-cec1e7c72428","resolution":{"observed_at":"2026-08-14T15:59:20.449790Z","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":"2026-08-14T15:59:20.862462Z","title":"Real-time foreground–background segmentation using codebook model,","venue":null,"work_id":"e2bf64ce-6921-4cf3-a779-6a0c5cdaf4f6","year":2005},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.454067Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:e67be2c55d569cdbd3537791fa55a2a1a40052e6c4f539615b639af961c52ccf","observation_id":"17e93f79-6c5e-41c4-9ea6-9367d44193b3","resolution":{"observed_at":"2026-08-14T15:59:20.866984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:59:20.845920Z","title":"Efﬁcient video object co- localization with co-saliency activated tracklets,","venue":null,"work_id":"ec7def1f-1c27-4247-9375-53636f63a65a","year":2018},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.458147Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:a8a71c0006101907b318654810140245ed20b65f5669a8052ee20a405ebb19db","observation_id":"1b8d4653-78c9-49c1-99c4-3b137579244b","resolution":{"observed_at":"2026-08-14T15:59:20.850337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:59:20.814752Z","title":"Uncertainty in deep learning,","venue":null,"work_id":"c2345495-431e-4099-a57a-fcc9514203b8","year":2016},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.462099Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:d4981910bd566e11cd87a3b78834a4553a3583581ea3560219400c6f0c8c3f7e","observation_id":"904c54b3-ef66-45da-85be-c5f8a685d0a7","resolution":{"observed_at":"2026-08-14T15:59:20.835196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:59:20.791474Z","title":"What uncertainties do we need in bayesian deep learning for computer vision?","venue":null,"work_id":"2de3a2f9-76c8-4d68-a6bc-8b0c728c815b","year":2017},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.466356Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:297e6825a6f0218cd9969b3383d2fea53ee22967220eee92f757c1fc1d2e1c02","observation_id":"ce338f25-82d0-46d2-b9ed-d00ceeee6c28","resolution":{"observed_at":"2026-08-14T15:59:20.796608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.04765","last_updated":"2018-03-13T13:02:13Z","snapshot_observed_at":"2026-08-14T19:36:43.134642Z","submitted_at":"2018-03-13T13:02:13Z","title":"Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.04765","snapshot_observed_at":"2026-08-14T15:59:20.470295Z","title":"Deep k-nearest neighbors: Towards conﬁdent, interpretable and robust deep learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.470295Z"},"links":{"cited_paper":"/paper/1803.04765","citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:f68967dd649a07b15d881d5131153936d431788687d9dc33f1edde2dff3c6397","observation_id":"42728a17-22be-474e-9a07-cc684b10cfec","resolution":{"observed_at":"2026-08-14T15:59:20.470295Z","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":"2026-08-14T15:59:20.775027Z","title":"Indoor segmentation and support inference from rgbd images,","venue":null,"work_id":"18dc3da1-2b96-4ab5-a5b7-8d0a42c4c830","year":2012},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.474614Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:e0c1aee2817c6f8729fa52c6c3353800641a74fb3fa89ae040bb59fcede9d133","observation_id":"da4bacfc-5c3c-4d7c-97a5-90871f1fbf81","resolution":{"observed_at":"2026-08-14T15:59:20.781130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:59:20.478796Z","title":"Very deep convolutional networks for large-scale image recognition,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.478796Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:81af54e0b5e3b35aa1fe8612c70f448c11a85a18c26322cd4e49d5eaeb62ebcd","observation_id":"b00cfb75-0f0a-44a6-9389-f82d5d82fa3a","resolution":{"observed_at":"2026-08-14T15:59:20.478796Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:59:20.482783Z","title":"Scene parsing through ade20k dataset,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.482783Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:386712642a96d652d8a7610b7bffdb7deca8e84b4d43d1668125587cd73c3db7","observation_id":"8ded341e-4df3-4f50-995e-53845bf24c87","resolution":{"observed_at":"2026-08-14T15:59:20.482783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1709.09844","last_updated":"2017-09-28T08:09:47Z","snapshot_observed_at":"2026-08-16T14:48:44.391997Z","submitted_at":"2017-09-28T08:09:47Z","title":"Distance-based Confidence Score for Neural Network Classifiers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.09844","snapshot_observed_at":"2026-08-14T15:59:20.486620Z","title":"Distance-based conﬁdence score for neural network classiﬁers,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.486620Z"},"links":{"cited_paper":"/paper/1709.09844","citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:c5a632b32f6e8f012b193bace097fd95ee9b14b45fdf6e43b98a576b01698473","observation_id":"867eb40c-fb9b-492b-bd01-cb223111e1ea","resolution":{"observed_at":"2026-08-14T15:59:20.486620Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:59:20.490919Z","title":"The pascal visual object classes (voc) challenge,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.490919Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:0eded9ad83b01b3e8c90b9e7299026ba8467988f7b5aaf8c31285b4ee8468afb","observation_id":"79a54a55-f1d1-42ea-ac2f-3a0de6a79f61","resolution":{"observed_at":"2026-08-14T15:59:20.490919Z","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":"2026-08-14T15:59:20.696368Z","title":"Fast k nearest neighbor search using gpu,","venue":null,"work_id":"d2fc2748-319e-46ae-8d7f-154946eb5155","year":2008},"citing_paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T15:59:20.495062Z"},"links":{"citing_paper":"/paper/1908.00448"},"observation_digest":"sha256:9f3121c230c92444fa77247d70b929f435ab396acee9ab057eb93c664c10c965","observation_id":"a0518a09-bfd9-4aa6-b307-e23697429417","resolution":{"observed_at":"2026-08-14T15:59:20.708039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.00448","last_updated":"2020-01-13T21:46:06Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T01:13:13.477137Z","submitted_at":"2019-08-01T15:03:05Z","title":"Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":15},"total_outbound_references":28},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:1908.00448."}