{"as_of":"2026-08-16T07:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3753debd7f85607540b4c9f3e71946230075081f2f0c3e39197d5c30432265df","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T13:31:26.100847Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"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/1908.04992/citation-record","integrity":"/paper/1908.04992/integrity","json":"/paper/1908.04992/citation-record.json","paper":"/paper/1908.04992"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:31:27.042693Z","title":"Multi-level factorisation net for person re-identification","venue":null,"work_id":"7593d5fb-51c9-4e6d-8a09-68a7fc2220a8","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.859705Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:55f1b0cc2cd9306fa510b401cff4e6623af9e9c000a102da521814c21200a348","observation_id":"5588bd10-ac15-4796-858a-e5bc9c9eb3f5","resolution":{"observed_at":"2026-08-14T13:31:27.048086Z","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":"2026-08-14T13:31:27.026265Z","title":"Group consistent similarity learning via deep crf for person re-identification","venue":null,"work_id":"050b8cf4-7a91-4e9a-a0f6-379e641a9141","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.865390Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:69da7ed8dfe617507fdaeed9bea232d38ad5235a58167fc2e0ca30dc51be5dd8","observation_id":"1735a10d-3bc2-4c60-bf05-3558733594c0","resolution":{"observed_at":"2026-08-14T13:31:27.031502Z","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":"2026-08-14T13:31:27.009782Z","title":"Beyond triplet loss: a deep quadruplet network for person re-identification","venue":null,"work_id":"3c3b1cec-f436-45b7-bac5-ff002fd23b00","year":2017},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.870485Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:ebdfedc351bd5d3f2828b2e8bd3cf77ff5cc70c60256f1a8b8fc520e0630ca71","observation_id":"afd72513-05e2-4839-82fa-8f9eee09d1b8","resolution":{"observed_at":"2026-08-14T13:31:27.014935Z","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":"2026-08-14T13:31:26.993337Z","title":"Person re-identification by deep learning multi-scale representa- tions","venue":null,"work_id":"dd1c219a-ec91-4513-8512-7450da39cc84","year":2017},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.876508Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:a4a193c0b45ba7aefaa5a34dd0ffb5b0022e5db3bebdf49a8a99df20c48e63dc","observation_id":"b2eb2c05-3e6e-491c-beeb-097c74856887","resolution":{"observed_at":"2026-08-14T13:31:26.998678Z","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":"2026-08-14T13:31:26.978486Z","title":"Diffusion processes for retrieval re- visited","venue":null,"work_id":"ba8270ed-559e-460f-99d6-44bc3be4ef3f","year":2013},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.881906Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:ba148e366a5fca740b06a9882e0438cc25ac98bd4b0ede5d621b1c908b589cc4","observation_id":"a942faa8-6a2f-4675-948e-3ddd0d8dadfd","resolution":{"observed_at":"2026-08-14T13:31:26.983382Z","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":"2026-08-14T13:31:26.963376Z","title":"Model- agnostic meta-learning for fast adaptation of deep networks","venue":null,"work_id":"4537899e-4ed7-4b0c-b49e-e790e4a45a93","year":2017},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.887251Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:8757e0c453f8cc3d39dc4ccc7ee4dbf9b9cabc382c740aa878e986f888dc704b","observation_id":"bfa27075-ea4e-4c91-8094-3adfc5494b2e","resolution":{"observed_at":"2026-08-14T13:31:26.968347Z","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":"2026-08-14T13:31:26.947846Z","title":"Where to buy it: Matching street clothing photos in online shops","venue":null,"work_id":"3a201365-971c-41d9-8d63-fc21482d274a","year":2015},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.893003Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:4dc4d8491f5b428f9fb8a94bcf4f66f18f7c7d610c89fcd1d68e305ff7bd4bd9","observation_id":"94b158b5-ae04-4a7e-9c2a-595cbb1c33f5","resolution":{"observed_at":"2026-08-14T13:31:26.952870Z","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":"2026-08-14T13:31:26.933181Z","title":"Dimension- ality reduction by learning an invariant mapping","venue":null,"work_id":"69ab6b66-068f-4c30-a77c-0dfa0b3d27aa","year":2006},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.897825Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:0b96052a0825178eb58121a53301fa649b0a59db9167f01a0c2aae1b7997a362","observation_id":"c09151bb-9c22-41da-a44e-d5936c3e59c0","resolution":{"observed_at":"2026-08-14T13:31:26.937841Z","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":"2026-08-14T13:31:26.918349Z","title":"Inductive representation learning on large graphs","venue":null,"work_id":"dfd74371-c4b6-4afa-98e2-e0f241838c71","year":2017},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.902508Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:5a8f98b85618230f62bd71798cf3aaa9d30d2b198803490609b46ea294ebd7bb","observation_id":"d519991f-9f67-43b4-a374-b15f2f522896","resolution":{"observed_at":"2026-08-14T13:31:26.923214Z","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":"2026-08-14T13:31:26.903314Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"99278436-44b7-4fff-b07e-d0a3dde1093c","year":2016},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.907385Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:e98f5d809c8e47b46aee0e258102144a625b4572bd1ffa7f19a68f429efb52d0","observation_id":"66621fff-97d2-4afc-9676-dc82aed9f408","resolution":{"observed_at":"2026-08-14T13:31:26.908065Z","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":"2026-08-14T13:31:26.887139Z","title":"Efficient diffusion on region manifolds: Recovering small objects with compact cnn representations","venue":null,"work_id":"e64954cc-d910-4d7f-b203-8c82211b5f49","year":2017},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.912296Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:d85ad67d0c2b4637a588f01c51c5d10a8a507719f7999b4954ba5e8ba0351122","observation_id":"a2eb2be0-562f-427d-be20-03cfb39571f7","resolution":{"observed_at":"2026-08-14T13:31:26.893091Z","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":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-14T13:31:25.917568Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.917568Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:5256cbd8c6353889736bd904fd89b29ac196c07772d8bbb5b1b41910921bcb00","observation_id":"c53f6eb4-7b27-45af-a073-953a8116b446","resolution":{"observed_at":"2026-08-14T13:31:25.917568Z","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":"2026-08-14T13:31:26.871478Z","title":"Deep- reid: Deep filter pairing neural network for person re- identification","venue":null,"work_id":"9781921a-b62e-4bf3-bd6f-ea112ca0223c","year":2014},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.923012Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:a80eaa79ff65915d8e585dc4f26b460de3fd19623c903893ce8cda0b06d85722","observation_id":"d042739a-9f5b-4d9c-989a-a834ce7fade6","resolution":{"observed_at":"2026-08-14T13:31:26.876440Z","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":"2026-08-14T13:31:26.856692Z","title":"Harmonious at- tention network for person re-identification","venue":null,"work_id":"5dd5e477-9404-456b-96e6-98a4e2ac7433","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.927961Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:d4048ca91a8689b06cb4aa21d75c63b68de159becfb7cb4776ef1d527d0e380b","observation_id":"15e977d9-d82e-4f11-b561-6bd3d548300d","resolution":{"observed_at":"2026-08-14T13:31:26.861611Z","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":"2026-08-14T13:31:26.841750Z","title":"Learning to propagate labels: Transductive propagation network for few-shot learn- ing","venue":null,"work_id":"4d76118c-d078-40e8-a158-5b99eca5de92","year":2019},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.932631Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:fbad782418f7accf1f4d4060258f7b98717e9d51c49a2bfa93f7e31da302ecdc","observation_id":"18152164-f503-49b1-8bb2-3cb93e43e9b9","resolution":{"observed_at":"2026-08-14T13:31:26.846721Z","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":"1811.11405","last_updated":"2018-11-28T06:46:38Z","snapshot_observed_at":"2026-08-14T17:52:14.184004Z","submitted_at":"2018-11-28T06:46:38Z","title":"Spectral Feature Transformation for Person Re-identification","version":1},"cited_work":{"arxiv_id":"1811.11405","doi":null,"metadata_source":"pith","pith_arxiv_id":"1811.11405","snapshot_observed_at":"2026-08-14T13:31:26.192402Z","title":"Spectral Feature Transformation for Person Re-identification","venue":"cs.CV","work_id":"2774466a-fc0c-4f15-bc56-f2949c1cdd78","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.937669Z"},"links":{"cited_paper":"/paper/1811.11405","citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:667c7916636fa99c19fe34eab8ab78424fe62f0123daa4e9972de1b61d893a4d","observation_id":"213c361e-ac4b-4b63-8985-2103f50557f7","resolution":{"observed_at":"2026-08-14T13:31:26.200139Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"1301.3781","last_updated":"2013-09-07T00:30:40Z","snapshot_observed_at":"2026-07-06T03:04:11.148340Z","submitted_at":"2013-01-16T18:24:43Z","title":"Efficient Estimation of Word Representations in Vector Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3781","snapshot_observed_at":"2026-08-14T13:31:25.942984Z","title":"Efficient estimation of word representations in vector space","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.942984Z"},"links":{"cited_paper":"/paper/1301.3781","citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:eaa8637e6d01fe36d32ffc9ec2f6c9ed31a67b760bf5c05227ecd876ff14520e","observation_id":"7c88a285-2869-47ec-94b3-bcf0ea8de68e","resolution":{"observed_at":"2026-08-14T13:31:25.942984Z","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":"2026-08-14T13:31:26.826597Z","title":"Deep metric learning via facility location","venue":null,"work_id":"efaec14c-f734-4a73-835d-31bf21a51699","year":null},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.948114Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:a0e39c55cc4b34da391842c5381c7d2f623db3c372d697f71ae5750a44f73304","observation_id":"d6a747c8-0435-4d33-ad27-5181a8591828","resolution":{"observed_at":"2026-08-14T13:31:26.831475Z","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":"2026-08-14T13:31:26.811713Z","title":"Deep metric learning via lifted structured feature embedding","venue":null,"work_id":"c5992d6c-5352-40e6-b651-9396c1bf3e77","year":2016},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.953343Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:4d62bdfd8abcdc1e1cbe78945586fac7175b096c342b9b9054ee5e6e20d769dd","observation_id":"f9934369-a12b-49a6-9319-8c3933c3b71d","resolution":{"observed_at":"2026-08-14T13:31:26.816425Z","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":"2026-08-14T13:31:26.795627Z","title":"Deep face recognition","venue":null,"work_id":"f907a862-cb42-4e1d-9341-61f74eb7e63c","year":2015},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.958514Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:fb90e155ea70417b038468df377a442e7b4dc8be28ec6a0a36776aa9afec7695","observation_id":"2ea5b600-5a6f-464f-a76a-043abf84d775","resolution":{"observed_at":"2026-08-14T13:31:26.801579Z","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":"2026-08-14T13:31:26.779599Z","title":"Deepwalk: Online learning of social representations","venue":null,"work_id":"3236302c-7df1-4316-bff4-d5472cef518d","year":2014},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.963502Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:8bb0812fdfe2af3b55d77be6d2955edf1f27d145aa550eb58060fd5bec1a8351","observation_id":"a1a1074e-a248-4939-b820-5095a59ca703","resolution":{"observed_at":"2026-08-14T13:31:26.784554Z","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":"2026-08-14T13:31:26.764219Z","title":"Optimization as a model for few-shot learning","venue":null,"work_id":"a31d3be8-82c4-4ef6-a7d6-385ba12f1ceb","year":2017},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.968179Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:eaacdfb6efe47dc7449e44ebece30fffa8d5b33283628bc57c7391524ab8e842","observation_id":"6f099405-d04b-4703-bc73-a5ff5a47806b","resolution":{"observed_at":"2026-08-14T13:31:26.769090Z","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":"2026-08-14T13:31:26.748304Z","title":"Tenenbaum, Hugo Larochelle, and Richard S","venue":null,"work_id":"e3181e16-2ea4-4385-a841-4ecd556fd5a7","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.972677Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:7c7f4a05c45ed5d152957d70ac4e9f0a72acf2c975d42ae10aaa294442267bea","observation_id":"18eb5ea3-d194-4ebb-9c1d-0c034ee7ff99","resolution":{"observed_at":"2026-08-14T13:31:26.753729Z","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":"2026-08-14T13:31:26.733257Z","title":"Performance measures and a data set for multi-target, multi-camera tracking","venue":null,"work_id":"f0312c1d-42d8-4e61-b2bd-4a56c3a71e1f","year":2016},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.977650Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:1997e0037f2ad3a3ed10143eb71f3f7cb32069a14c85b4fa746d05d91e9b47f5","observation_id":"5606f1b1-8a8a-4a41-8a27-2153195a9a28","resolution":{"observed_at":"2026-08-14T13:31:26.738054Z","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":"2026-08-14T13:31:26.717707Z","title":"Imagenet large scale visual recognition challenge","venue":null,"work_id":"a2a5ef56-dadf-44d4-8863-99ba2d441356","year":2015},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.982515Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:2fc9a986aaa93516e7a5ec78cc782b464b9b8154558b30aafa6d5d576ca1e495","observation_id":"310d6c43-310a-4706-8a8c-e4a46c4a3f4f","resolution":{"observed_at":"2026-08-14T13:31:26.722881Z","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":"2026-08-14T13:31:26.700775Z","title":"Facenet: A unified embedding for face recognition and clus- tering","venue":null,"work_id":"266d8c6c-ef38-41c4-9bf8-7042d04ccc51","year":2015},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.987677Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:6ecd37ab6d554f4b9f1ce5c24572c52b1de5a854acf4bad3bff3175de93541da","observation_id":"f965d2fe-1cfe-463c-b740-50e3efa7e657","resolution":{"observed_at":"2026-08-14T13:31:26.706679Z","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":"2026-08-14T13:31:26.684732Z","title":"Deep group-shuffling random walk for person re-identification","venue":null,"work_id":"9ba5b6ea-3150-481b-923e-210ab224f2c2","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.992327Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:6f04b3bd5937564093783e4acfbddde101b9caa96f350ddc9a8daf117fcd7f60","observation_id":"eac68d1b-441c-48fa-aac7-ae917efdfccd","resolution":{"observed_at":"2026-08-14T13:31:26.689977Z","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":"2026-08-14T13:31:26.669061Z","title":"Person re-identification with deep similarity-guided graph neural network","venue":null,"work_id":"3ff62e66-d989-4aac-8498-83ea49bbc306","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:25.997241Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:f01bd7bd4fc64465e20a04f5e2451bdf0802e52ad5affd80cbb3552df2dcc841","observation_id":"89f13bfe-de22-430b-ba5f-84294bc1f7dd","resolution":{"observed_at":"2026-08-14T13:31:26.674257Z","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":"2026-08-14T13:31:26.653674Z","title":"Dual attention matching network for context-aware feature sequence based person re-identification","venue":null,"work_id":"c4f61beb-9594-4e50-afe5-ba4e00064777","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.003319Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:5b28773ca3157b05217b6d5a864f1e9830c1951afa49dd8de7675a5a498dc881","observation_id":"e260875a-c1ad-41bc-8c92-9e2ff7022b17","resolution":{"observed_at":"2026-08-14T13:31:26.658760Z","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":"2026-08-14T13:31:26.638072Z","title":"Very deep convo- lutional networks for large-scale image recognition","venue":null,"work_id":"c730effb-01d5-4507-8547-ce17242fa3d3","year":2015},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.008429Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:e291d5219a7d8c941557285793792160f9a76f4fb5ea84294d363c2f2273de64","observation_id":"f84d0ebf-9aed-4b34-90b7-da31384f7387","resolution":{"observed_at":"2026-08-14T13:31:26.643239Z","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":"2026-08-14T13:31:26.622402Z","title":"Prototypi- cal networks for few-shot learning","venue":null,"work_id":"a142b462-a5a7-47ad-9dc5-9a01d27ad7da","year":2017},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.013158Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:cb68ea3a15ff8a2ce1c559f92068576692da005c578985f6c1112fe2fc3c113f","observation_id":"71b14ac6-925d-4854-b84e-489018fa082d","resolution":{"observed_at":"2026-08-14T13:31:26.627693Z","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":"2026-08-14T13:31:26.605569Z","title":"Memory-based parameter adaptation","venue":null,"work_id":"53e4ca48-23a8-48dd-b07a-7c15eeaec28a","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.018034Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:61bddaa70c0aabc9362d0c2838e81fbeb7b4008a5497cd36f32add235e8c0bff","observation_id":"b1248997-f9fa-4193-a2fd-4407863d12ab","resolution":{"observed_at":"2026-08-14T13:31:26.610676Z","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":"2026-08-14T13:31:26.589342Z","title":"Part-aligned bilinear representations for per- son re-identification","venue":null,"work_id":"24563822-a80e-4d9b-815f-6680d84edfaa","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.023273Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:f31efe9f7369e7dad5f2b81ce4a0e776d1a91e937354b0ff77413c2cba8201a6","observation_id":"126e3e4b-4d60-468f-8098-2dcd0814d0c3","resolution":{"observed_at":"2026-08-14T13:31:26.594861Z","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":"2026-08-14T13:31:26.420935Z","title":"End-to-end memory networks","venue":null,"work_id":"6dd0911d-8d43-4f44-8de5-7fc33ed9b195","year":2015},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.028008Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:e6ffa14a13f35b048227b079812b1c5d3bcbb705aaa688bc2fbe0306e6728099","observation_id":"317636c9-a1a8-4ad7-a142-8d5b1c373a3b","resolution":{"observed_at":"2026-08-14T13:31:26.426104Z","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":"2026-08-14T13:31:26.405400Z","title":"Svdnet for pedestrian retrieval","venue":null,"work_id":"f20625e8-fa05-4dbd-8061-66a070b9e608","year":2017},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.033521Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:357cdc9e50bcecd17cf19dff48590a21763949d244c8a85e3f4c9f82610412ca","observation_id":"57d1e75b-610b-46cd-8423-59b558f386d1","resolution":{"observed_at":"2026-08-14T13:31:26.410397Z","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":"2026-08-14T13:31:26.389479Z","title":"Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline)","venue":null,"work_id":"61725442-1396-440e-9274-bc84c9013e98","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.038722Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:4ad38726d58e4e18b5758e8931da9b8ca6ef69fdd74107daaeba2002093b48fd","observation_id":"d55e4f8c-0278-4552-89bd-aa9b67f124ec","resolution":{"observed_at":"2026-08-14T13:31:26.395347Z","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":"2026-08-14T13:31:26.373947Z","title":"Learning to compare: Re- lation network for few-shot learning","venue":null,"work_id":"f875ebb7-703e-49f9-9460-e43d7e3ab2ae","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.044235Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:7c800bd777db7f4d4bde48e93beced6db26d25624cf70478ac83541aa6762cc5","observation_id":"ad6a9cf1-0616-4652-8e0a-e5e5662ee9b3","resolution":{"observed_at":"2026-08-14T13:31:26.378878Z","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":"2026-08-14T13:31:26.358338Z","title":"Going deeper with convolutions","venue":null,"work_id":"db630463-f241-46c4-ac4f-5cff8b3d6ba2","year":2015},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.049359Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:341d01761d7d1a24c38fb484e70d2446e1af0d9f7d3aee84d6bac997bdbea5e9","observation_id":"f6c8447f-7e54-4326-afef-6517bc968c8b","resolution":{"observed_at":"2026-08-14T13:31:26.363504Z","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":"2026-08-14T13:31:26.342889Z","title":"Better matching with fewer features: The selection of useful features in large database recognition problems","venue":null,"work_id":"5f6d347a-e348-41ee-bd9a-8b0d59b7fa6f","year":2009},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.055153Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:94ee98d735c56f374048a73ba126c57320ed41ef13ef1a83048834699a212dda","observation_id":"d830908d-d7d1-4d71-b703-0e1c5c313739","resolution":{"observed_at":"2026-08-14T13:31:26.347961Z","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":"2026-08-14T13:31:26.326732Z","title":"Graph at- tention networks","venue":null,"work_id":"f90eba29-e4de-4e6d-a57d-c647aa07c93f","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.060447Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:055caf65f1e33e19ac3214cd9d02a3aeee7b9af6665674594ff9ce3340daf617","observation_id":"9b575634-ebaf-47f2-aa14-461c6d5fa00c","resolution":{"observed_at":"2026-08-14T13:31:26.332112Z","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":"2026-08-14T13:31:26.310316Z","title":"Matching networks for one shot learning","venue":null,"work_id":"085bb0ca-d9a0-487a-96ee-2b6e3174c74e","year":2016},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.065260Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:230592890f748da9c3c65ceab1d2fd942850063aff5552aa42d64d08f586a08e","observation_id":"5901415b-bcdb-40a1-8230-5659f51d95d8","resolution":{"observed_at":"2026-08-14T13:31:26.315332Z","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":"2026-08-14T13:31:26.292749Z","title":"Mancs: A multi-task attentional network with curriculum sampling for person re-identification","venue":null,"work_id":"2f69c591-e70f-4d34-bd9c-144f494184dd","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.070224Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:0ed91d49c99ef9118419211dbf3f59bd05f853428405a8dc1cabe5fe1da46474","observation_id":"c96c969c-2f9b-4746-8f3b-446b663d57fc","resolution":{"observed_at":"2026-08-14T13:31:26.298057Z","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":"2026-08-14T13:31:26.277113Z","title":"Normface: l 2 hypersphere embedding for face ver- ification","venue":null,"work_id":"92b5ef15-771c-4ca7-a5d1-9d47b10d0073","year":null},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.074960Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:e19dfde791ec2cbba16ddaaf4012fa48e7a42865f52922a7f680eb0208f20323","observation_id":"6ee74a97-df55-4b48-9985-c56833159a78","resolution":{"observed_at":"2026-08-14T13:31:26.281896Z","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":"2026-08-14T13:31:26.261146Z","title":"Resource aware person re-identification across multiple resolutions","venue":null,"work_id":"fe7b606c-5fdb-4e66-9ebd-6ac0e475685b","year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.080154Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:f6d3c20f54a26faee96ec35983591122b13e3462cc7b69cc8b71bdec365a1f6e","observation_id":"81ff01ff-33dd-4e7d-b147-cb7d4763199c","resolution":{"observed_at":"2026-08-14T13:31:26.266429Z","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":"2026-08-14T13:31:26.245004Z","title":"Distance metric learning for large margin nearest neighbor classification","venue":null,"work_id":"d3ce205b-a2c4-4618-a6d4-02b63b6028a5","year":2006},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.084951Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:75230d237de802d7ff8d045f94848d2ee8c843dfbe44dee67fcfb91141a8b3fe","observation_id":"f7e612e9-eb0a-4616-9214-52c3fe540bf7","resolution":{"observed_at":"2026-08-14T13:31:26.250241Z","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":"1812.03664","last_updated":"2021-06-13T06:16:30Z","snapshot_observed_at":"2026-08-14T17:46:11.796697Z","submitted_at":"2018-12-10T07:55:56Z","title":"Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.03664","snapshot_observed_at":"2026-08-14T13:31:26.089963Z","title":"Learning embedding adaptation for few-shot learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.089963Z"},"links":{"cited_paper":"/paper/1812.03664","citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:23dc49b11064f84c03ae5072b714c913502f5901876b4acfcc81efa1a5247b84","observation_id":"9c821279-da4d-41e6-9c94-bbd5f9b37e63","resolution":{"observed_at":"2026-08-14T13:31:26.089963Z","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":"2026-08-14T13:31:26.228772Z","title":"Re- ranking person re-identification with k-reciprocal encoding","venue":null,"work_id":"9efdd52f-65f1-4c85-a1e8-d228d45306a3","year":2017},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.096097Z"},"links":{"citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:4962cb5152b826b556e307d3f0e73c85694b59f843d58180a058ef4a1f9d890e","observation_id":"19a71b85-8425-4ac9-90e0-0d946abadd59","resolution":{"observed_at":"2026-08-14T13:31:26.233700Z","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":"1708.04896","last_updated":"2017-11-16T10:05:35Z","snapshot_observed_at":"2026-08-14T20:41:04.759433Z","submitted_at":"2017-08-16T13:56:48Z","title":"Random Erasing Data Augmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.04896","snapshot_observed_at":"2026-08-14T13:31:26.100847Z","title":"Random erasing data augmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T13:31:26.100847Z"},"links":{"cited_paper":"/paper/1708.04896","citing_paper":"/paper/1908.04992"},"observation_digest":"sha256:ddbaf8f40e3e16bc6bb524c190264c7da90b53e4cd36bab77874a56f452b056f","observation_id":"83a6112d-2948-45c2-8661-0e25155d5d90","resolution":{"observed_at":"2026-08-14T13:31:26.100847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.04992","last_updated":"2019-08-14T07:19:12Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T13:24:23.011217Z","submitted_at":"2019-08-14T07:19:12Z","title":"Memory-Based Neighbourhood Embedding for Visual Recognition"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":1,"verified_fuzzy":43},"total_outbound_references":48},"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 16 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:1908.04992."}