{"as_of":"2026-08-21T08:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:24950538660438a10954320efee649bf370091c4b4e90da15fd6a72755b87412","coverage":[{"denominator":143,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T10:12:09.401456Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/1908.11820/citation-record","integrity":"/paper/1908.11820/integrity","json":"/paper/1908.11820/citation-record.json","paper":"/paper/1908.11820"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T10:12:08.871967Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.871967Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:b404da8b8079b7d4ae99c1308655907ba18ebd1402aa26f41b8b08c1f079864f","observation_id":"10aa2b44-3f11-467b-9727-4c32c0c36b28","resolution":{"observed_at":"2026-08-14T10:12:08.871967Z","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":"2026-08-14T10:12:08.878822Z","title":"Shotton, J","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.878822Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:0c41335f1957ac63134e6618fbd3acb8b82866c280ee3a6cca3f8a54296665ff","observation_id":"c2f64dad-b77d-4c31-887f-5226097389cc","resolution":{"observed_at":"2026-08-14T10:12:08.878822Z","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":"2026-08-14T10:12:08.884215Z","title":"Efficient inference in fully connected crfs with gaussian edge potentials","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.884215Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:61684cdbfdf1df1644d3421b9e4da8f06e3fba81c5e33bebe4eab11a1fa05f2f","observation_id":"537a6479-5adf-43b4-af7f-bb76deb80da5","resolution":{"observed_at":"2026-08-14T10:12:08.884215Z","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":"2026-08-14T10:12:08.890204Z","title":"Ladick`y, C","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.890204Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:ed33a2afa40f66aae7ab4ef35455ffda0cd355a4fd2dba4c47ac8a032a60ab66","observation_id":"60fb76df-e358-47a0-acee-6b0f0999499d","resolution":{"observed_at":"2026-08-14T10:12:08.890204Z","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":"2026-08-14T10:12:08.895718Z","title":"Pylon model for semantic segmentation","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.895718Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:e94aedb6a4b9754f622e143299ab1d07390a25898ea38e74d8dbd8b6cb1d990e","observation_id":"a7679524-5544-4b2d-a54f-3eb16dbc93a7","resolution":{"observed_at":"2026-08-14T10:12:08.895718Z","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":"2026-08-14T10:12:08.901183Z","title":"Discriminative re-ranking of diverse segmentations","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.901183Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:3078fc0a3d2f47f9681727688a5d48f92e36138e32f8f0dc375212713c57e59a","observation_id":"ced941af-1948-42ea-8efc-a1daeab5fe2b","resolution":{"observed_at":"2026-08-14T10:12:08.901183Z","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":"2026-08-14T10:12:08.907193Z","title":"Diverse M-Best Solutions in Markov Random Fields","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.907193Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:f22cd0e9a9dbb01cc9d967159ee5c17dffd0ec630f17b837eb06ba530f0db206","observation_id":"a68c6315-08fa-440a-869c-0496174f819b","resolution":{"observed_at":"2026-08-14T10:12:08.907193Z","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":"2026-08-14T10:12:08.911798Z","title":"Gonfaus, Joost van de Weijer, Andrew D","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.911798Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:b10d4a8c5beb5a691b87bc99151bcce3a84346c4c2c5680a67ac1249f08bf017","observation_id":"bf6b6dc8-9b6a-49c6-8bf5-f8df31ff5288","resolution":{"observed_at":"2026-08-14T10:12:08.911798Z","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":"2026-08-14T10:12:08.916716Z","title":"A robust multilevel segment de- scription for multi-class object recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.916716Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:c7970964eea746e248e3b7eaa205667d6cee222a5c8e2fbcc5b9413eddaaf828","observation_id":"734f2615-1da0-4788-9bc0-606b5cab3f0f","resolution":{"observed_at":"2026-08-14T10:12:08.916716Z","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":"2026-08-14T10:12:08.922278Z","title":"Kolkin, and Gregory Shakhnarovich","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.922278Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:62877dcf917a5854dc90e698eb31fcfd9054ee0dbbe54e7b2108c7c954dae6e2","observation_id":"634946e9-ae90-4ee6-8698-3c866515707f","resolution":{"observed_at":"2026-08-14T10:12:08.922278Z","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":"2026-08-14T10:12:08.927113Z","title":"ImageNet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.927113Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:25676e43697ca5032fd999f7aba73ee1315508b24dbe1fc5e35b85a76a2b712c","observation_id":"bf0daa0c-798d-4a3b-bab3-a3a7df9d5ecb","resolution":{"observed_at":"2026-08-14T10:12:08.927113Z","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":"2026-08-14T10:12:08.932766Z","title":"Semantic image segmentation via deep parsing network","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.932766Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:393a250c97ff0a1584ec587bc65640e6f5307155828b9cb4d51d2161ac6fe53a","observation_id":"1176a727-c2b6-4132-966c-864927774ae9","resolution":{"observed_at":"2026-08-14T10:12:08.932766Z","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":"2026-08-14T10:12:08.944628Z","title":"Pyramid scene parsing network","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.944628Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:1f7571b4f987d084a3597a122daee4a0befa8732e878576d636e8bf7a3fbc360","observation_id":"f550a6ba-3525-4056-a7d7-2c6a86c24fae","resolution":{"observed_at":"2026-08-14T10:12:08.944628Z","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":"2026-08-14T10:12:08.951254Z","title":"The PASCAL Visual Object Classes (VOC) challenge","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.951254Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:c3c6a0ea7d4c38e00c6bf50599e3ca595881961b919f4efed531ba81b2bff38c","observation_id":"09d42e73-7795-431e-b6a2-e31b0215227d","resolution":{"observed_at":"2026-08-14T10:12:08.951254Z","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":"2026-08-14T10:12:08.956891Z","title":"Lawrence Zitnick","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.956891Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:dd6ab68a2fb0959d83d6751b098fee783578f1ed7ce7fa473738eb65b89c6569","observation_id":"44b101d7-207c-43cf-8f4b-1c5ed56e233b","resolution":{"observed_at":"2026-08-14T10:12:08.956891Z","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":"2026-08-14T10:12:08.962470Z","title":"Regularizing 109 deep networks by modeling and predicting label structure","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.962470Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:70c15bebaaa566b402e6ea693d300b3e0e7133bb17eb0056ba6173dd7af53b10","observation_id":"f9b57957-cf27-4bfb-8789-2df034b7b3fd","resolution":{"observed_at":"2026-08-14T10:12:08.962470Z","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":"2026-08-14T10:12:08.967964Z","title":"Arbelaez, M","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.967964Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:12603c7169cafeab0d37d9d96d092cf202e8f71d3639bb29681f89d558a94448","observation_id":"eeb3699f-21be-4c5d-a83f-8af115d0d970","resolution":{"observed_at":"2026-08-14T10:12:08.967964Z","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":"2026-08-14T10:12:08.975457Z","title":"Martin, C","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.975457Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:fef68cc354380db23086ade3618699bd44069fd2ab8cccc59c706057b1abcc54","observation_id":"f8e3642f-1033-4e3f-8bcf-09829d234bf9","resolution":{"observed_at":"2026-08-14T10:12:08.975457Z","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":"2026-08-14T10:12:08.981270Z","title":"Slic superpixels compared to state-of-the-art superpixel methods","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.981270Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:d081793296eefa25136d93e8f67578b0cfbca0c57b0fa97e77b1a7c43231b360","observation_id":"9f900aca-0b4a-45f1-8758-a00e412fb0e0","resolution":{"observed_at":"2026-08-14T10:12:08.981270Z","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":"2026-08-14T10:12:08.987579Z","title":"Comaniciu and P","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.987579Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:426801ecc57cfe2ed0a77f89646271d296a9ed6d566693ef1ea154661c630eae","observation_id":"14a4884f-54cf-4a52-99a8-450b3f6b4f04","resolution":{"observed_at":"2026-08-14T10:12:08.987579Z","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":"2026-08-14T10:12:08.993222Z","title":"Carreira and C","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.993222Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:8d2c7ba6bd58bad9d4b2970b86c49a125d85f8ba4c21163ec50933603828cd40","observation_id":"b40286a9-ba94-4fc5-a641-112a1986a208","resolution":{"observed_at":"2026-08-14T10:12:08.993222Z","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":"2026-08-14T10:12:08.999897Z","title":"Normalized cuts and image segmentation","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:08.999897Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:396b116a38a627ff00a220a8d3f63017055246d209d5f4382dc6e3de6f40ae86","observation_id":"456feee6-08a4-419d-a0f2-9f617e293300","resolution":{"observed_at":"2026-08-14T10:12:08.999897Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-14T10:12:09.005485Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.005485Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:3679f44cf321c26478032a26124d561867140ff505f0b17166a495ec68930ce9","observation_id":"7c9bc677-9c8c-4230-a0c3-d43088a6aea6","resolution":{"observed_at":"2026-08-14T10:12:09.005485Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.08506","last_updated":"2017-11-22T21:06:13Z","snapshot_observed_at":"2026-08-17T19:30:41.628384Z","submitted_at":"2017-11-22T21:06:13Z","title":"W-Net: A Deep Model for Fully Unsupervised Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.08506","snapshot_observed_at":"2026-08-14T10:12:09.010770Z","title":"W-net: A deep model for fully unsupervised image segmen- tation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.010770Z"},"links":{"cited_paper":"/paper/1711.08506","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:f2f65eab71699f0d40daaff263a214c252e8e7ddbc490ff21e14f0b4fd8c5786","observation_id":"08e67d93-ef26-44b1-942d-a3d7c8e4cad0","resolution":{"observed_at":"2026-08-14T10:12:09.010770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.00868","last_updated":"2019-04-10T18:17:40Z","snapshot_observed_at":"2026-08-14T19:58:26.470531Z","submitted_at":"2018-01-03T00:21:31Z","title":"Panoptic Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.00868","snapshot_observed_at":"2026-08-14T10:12:09.017388Z","title":"Girshick, Carsten Rother, and Piotr Dollár","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.017388Z"},"links":{"cited_paper":"/paper/1801.00868","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:6ece2b089ce346d2a1777e8377f187ecd45156287d4cced992fa0037f00f739a","observation_id":"c7b5391a-9e62-4525-809b-ebd843b46540","resolution":{"observed_at":"2026-08-14T10:12:09.017388Z","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":"2026-08-14T10:12:09.023144Z","title":"Cpmc: Automatic object segmentation using constrained parametric min-cuts","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.023144Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:5863208826339084968a0c8ac06d4820d4fbc8eb9f22b12705e451a6a9da7327","observation_id":"d2acb058-1903-4959-ad1a-b851e795b11d","resolution":{"observed_at":"2026-08-14T10:12:09.023144Z","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":"2026-08-14T10:12:09.028322Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.028322Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:f27d5ffa72660cf5eb4702d67cf12b9617e269eab5604d2ca78d41b1fe4c2f68","observation_id":"9581bc17-4a4f-4418-83b1-ad6f075baa2c","resolution":{"observed_at":"2026-08-14T10:12:09.028322Z","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":"2026-08-14T10:12:09.033358Z","title":"Arbelaez, B","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.033358Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:12cf114aebecac84176b9792d949878147a71db863b6fc0b622839ace1e51a47","observation_id":"9640df81-35e6-4d91-bf0e-e04d24df0e52","resolution":{"observed_at":"2026-08-14T10:12:09.033358Z","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":"2026-08-14T10:12:09.038178Z","title":"Semantic segmentation with second-order pooling","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.038178Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:0aa59abf875cd984fe89ad0feafc7d167d854ff4b8ebdf63b9f80e160dde9eaa","observation_id":"cabf6eef-2f7d-419c-9632-d4fbf2a61a69","resolution":{"observed_at":"2026-08-14T10:12:09.038178Z","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":"2026-08-14T10:12:09.043093Z","title":"Probabilistic joint image segmentation and labeling","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.043093Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:b90f8e56f91fdd1fe5c7c4065d8543c127fd672b06ee29972acc714fc95ef8c4","observation_id":"a6dcbc09-8e82-42b4-ad7a-1f2b515083d7","resolution":{"observed_at":"2026-08-14T10:12:09.043093Z","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":"2026-08-14T10:12:09.047854Z","title":"Object recognition by sequential figure-ground ranking","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.047854Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:22372269928ba3c7d1f4e79f700001d354bd1776f887d9cd3b72077289fad745","observation_id":"9de984d9-ded1-4076-a3da-e4cf4a758cfa","resolution":{"observed_at":"2026-08-14T10:12:09.047854Z","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":"2026-08-14T10:12:09.052410Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.052410Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:bb90cbd6590fc4caa71bd4d3ab3357fa949d1a864e3d04637e7a40bc52cd0547","observation_id":"404cbe96-6d8e-407d-ba10-5f8571f23020","resolution":{"observed_at":"2026-08-14T10:12:09.052410Z","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":"2026-08-14T10:12:09.057748Z","title":"Simultaneous detection and segmentation","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.057748Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:6d11df1a91a30aaeb22108f1616a1c4e94c36f3d23d59411523125334f0e78ed","observation_id":"2c3cb84c-febe-4c5f-9812-cf178d1ddd2a","resolution":{"observed_at":"2026-08-14T10:12:09.057748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.4313","last_updated":"2014-12-16T04:39:33Z","snapshot_observed_at":"2026-08-18T08:49:15.038008Z","submitted_at":"2014-12-14T05:23:39Z","title":"Combining the Best of Graphical Models and ConvNets for Semantic Segmentation","version":2},"cited_work":{"arxiv_id":"1412.4313","doi":null,"metadata_source":"pith","pith_arxiv_id":"1412.4313","snapshot_observed_at":"2026-08-14T10:12:10.106672Z","title":"Combining the Best of Graphical Models and ConvNets for Semantic Segmentation","venue":"cs.CV","work_id":"49f5375a-f1e2-41bb-81a0-da7e0452f980","year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.062811Z"},"links":{"cited_paper":"/paper/1412.4313","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:1b3901f97ab95a4aa4469e68cf6c2ed8868f2697bf5b44b03b0c9cb294c24dc9","observation_id":"58b0eb38-aef1-4c55-8c67-b6b854800746","resolution":{"observed_at":"2026-08-14T10:12:10.112547Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:09.068858Z","title":"Class segmentation and object localization with superpixel neighborhoods","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.068858Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:8688bfd8353d027293e91b0d5c5320b86d430ced1c44bf326a4a3d312c70b16f","observation_id":"52dd6b96-59be-48c4-8403-db829130b937","resolution":{"observed_at":"2026-08-14T10:12:09.068858Z","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":"2026-08-14T10:12:09.073383Z","title":"Context by region ancestry","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.073383Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:0acd98e8de47b3b081989844f94227e5d4be46ce8abdf45874b0941661197d9d","observation_id":"5ae47ebf-47ae-4414-bff1-6381c9974684","resolution":{"observed_at":"2026-08-14T10:12:09.073383Z","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":"2026-08-14T10:12:09.078286Z","title":"Are spatial and global constraints really necessary for segmentation? In ICCV, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.078286Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:c194d85e039f7f2558d5b9a533d4ec5bf8cac62d3384c0107c5244f538b575e9","observation_id":"ac4b692d-ef23-4acf-94d1-c94ce95635bb","resolution":{"observed_at":"2026-08-14T10:12:09.078286Z","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":"2026-08-14T10:12:09.083654Z","title":"Learning hierarchical features for scene labeling","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.083654Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:b33d2b9722ad9151a4ace69c9d8a4a369e6d4ea5d356cd397ebb6fa7e534819c","observation_id":"dd6ca295-7a4d-4b10-a5e3-41c103a4243c","resolution":{"observed_at":"2026-08-14T10:12:09.083654Z","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":"2026-08-14T10:12:09.088325Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.088325Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:efef87b75b2db41aeb8df222d7de1dcbc49566edc9b12102cffe9668226815a8","observation_id":"81532b62-7310-4543-99c5-56319c0cbc53","resolution":{"observed_at":"2026-08-14T10:12:09.088325Z","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":"2026-08-14T10:12:09.093455Z","title":"Lin, Andrew Y","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.093455Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:94e6c3f202f38ebe37b0ef38d853e0591eace35c5f7e6ec87c3be41adf96e88b","observation_id":"7a2fca36-5061-4518-a7bc-3c6d5f6021b8","resolution":{"observed_at":"2026-08-14T10:12:09.093455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1310.0354","last_updated":"2013-12-06T17:00:03Z","snapshot_observed_at":"2026-08-18T15:15:15.490733Z","submitted_at":"2013-10-01T15:42:54Z","title":"Deep and Wide Multiscale Recursive Networks for Robust Image Labeling","version":3},"cited_work":{"arxiv_id":"1310.0354","doi":null,"metadata_source":"pith","pith_arxiv_id":"1310.0354","snapshot_observed_at":"2026-08-14T10:12:10.083446Z","title":"Deep and Wide Multiscale Recursive Networks for Robust Image Labeling","venue":"cs.CV","work_id":"d4e52bc1-7827-4e5a-9358-2ec5035c2423","year":2013},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.099017Z"},"links":{"cited_paper":"/paper/1310.0354","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:c5acce7539a7e7d493e54314eacb81001b95cec1232b6707d000a02f60101a55","observation_id":"b6b595dd-7c5f-454e-a314-652d2d0ec5d8","resolution":{"observed_at":"2026-08-14T10:12:10.089046Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:09.104917Z","title":"Fully convolutional networks for semantic segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.104917Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:ab122b0e35318c00e8672b4e99c35e2dfd9affb663af478b0cc97fda35d13abe","observation_id":"6933e385-a561-482e-ae63-44d2ff905546","resolution":{"observed_at":"2026-08-14T10:12:09.104917Z","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":"2026-08-14T10:12:09.110440Z","title":"Hypercolumns for object segmentation and fine-grained localization","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.110440Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:c38f1b6bdacc73f7507d879044ea1a06f369a313110e14112a272df7803451f6","observation_id":"0b3c5af8-2988-4e43-a904-461ae4e997ab","resolution":{"observed_at":"2026-08-14T10:12:09.110440Z","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":"2026-08-14T10:12:09.115282Z","title":"Slic superpixels compared to state-of-the-art superpixel methods","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.115282Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:dd22c76c048f7a8604504de36fd574a8f62fed2b588468689bd6650ad21759f1","observation_id":"c0a8bc68-89dd-45a1-abd8-92261771bfb1","resolution":{"observed_at":"2026-08-14T10:12:09.115282Z","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":"2026-08-14T10:12:09.120174Z","title":"Williams, John Winn, and Andrew Zisserman","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.120174Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:0902e39346f55b40c022a809adc7e84ba07b8fb142bd04200ea7f555bdba0524","observation_id":"831e03e1-3e11-4df5-ad9f-6747b80635fe","resolution":{"observed_at":"2026-08-14T10:12:09.120174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.0575","last_updated":"2015-01-30T01:23:59Z","snapshot_observed_at":"2026-08-14T23:21:09.382166Z","submitted_at":"2014-09-01T22:29:38Z","title":"ImageNet Large Scale Visual Recognition Challenge","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.0575","snapshot_observed_at":"2026-08-14T10:12:09.125062Z","title":"Imagenet large scale visual recognition challenge","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.125062Z"},"links":{"cited_paper":"/paper/1409.0575","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:df645aeb40ad440b5175cee9110a13d0bc7829ef608fb98b4d9ab9c0b06efd0d","observation_id":"fd9c8e71-912e-4e55-aa35-7fc8f0d0569d","resolution":{"observed_at":"2026-08-14T10:12:09.125062Z","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":"2026-08-14T10:12:09.130241Z","title":"Structured output learning with high order loss functions","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.130241Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:79882b812b16dab37a6396f15ba052dc57e8ed1c7b79e6db1d557efb2d377433","observation_id":"d88a7cea-6d8f-4027-ba1e-526d9004bd38","resolution":{"observed_at":"2026-08-14T10:12:09.130241Z","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":"2026-08-14T10:12:09.135255Z","title":"Segmentation propagation in imagenet","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.135255Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:eea9cb329d3ea81ecae533c03c633c57d24a2f7c3c625dd30441d2ac6892e59f","observation_id":"0877635d-3397-47a3-8628-567ecedf7c48","resolution":{"observed_at":"2026-08-14T10:12:09.135255Z","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":"2026-08-14T10:12:09.140246Z","title":"Semantic contours from inverse detectors","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.140246Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:63cceee60133d4760889b2770610e8f60723e166611c4f1bb30730fce0424124","observation_id":"5289248f-4f43-400b-82e6-9b7b35b542a2","resolution":{"observed_at":"2026-08-14T10:12:09.140246Z","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":"2026-08-14T10:12:09.144913Z","title":"Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.144913Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:d61250c20c1a899331e3233b58ce9fbd1edd88fa7bbff6fe6b66eba7893fe84f","observation_id":"96dbcb81-77c0-47b7-9da1-89e3a15b0ba8","resolution":{"observed_at":"2026-08-14T10:12:09.144913Z","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":"2026-08-14T10:12:09.149585Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.149585Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:46592701d2aaf1767404e2508c9bdafbf0ebe092b65be6208445fa7b09ce26d5","observation_id":"7f553241-05ed-4278-b68a-77a23f20b833","resolution":{"observed_at":"2026-08-14T10:12:09.149585Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-14T10:12:09.154661Z","title":"Chatfield, K","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.154661Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:64091b97a9dfdc0197263deb1ad75014f46c6733bc567db55da50cec4ec2255a","observation_id":"e4a54b25-8d00-42c0-bc5e-91e78a12d40d","resolution":{"observed_at":"2026-08-14T10:12:09.154661Z","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":"2026-08-14T10:12:09.159434Z","title":"Simonyan and A","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.159434Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:9e8383db42f8c328f274798804f9284eab0315e300e4ccb9bb5efafd20000339","observation_id":"22f8a102-7407-4642-81de-a56bf01cc933","resolution":{"observed_at":"2026-08-14T10:12:09.159434Z","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":"2026-08-14T10:12:09.164165Z","title":"Girshick, J","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.164165Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:ce4034014bbec2ea10fa56cc35eae8058812276f27cf9608ad80ed6c480c10d7","observation_id":"01c95514-8b82-474c-bf5a-5adcc7d7245b","resolution":{"observed_at":"2026-08-14T10:12:09.164165Z","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":"2026-08-14T10:12:09.168780Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.168780Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:a444c9700f1039165e271456f8a990ada167a63d3d4f93128929f462c633170d","observation_id":"5d1dd81d-2976-4458-ab54-13cce16fbb55","resolution":{"observed_at":"2026-08-14T10:12:09.168780Z","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":"2026-08-14T10:12:09.173414Z","title":"Weinberger","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.173414Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:27e0a5bf4716e805f9e6449b17c6dcaf75b81ef57f4b3d3ce7ea6e8c01dfebfc","observation_id":"beff0554-f022-4863-ba31-362d1c876c4b","resolution":{"observed_at":"2026-08-14T10:12:09.173414Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.07012","last_updated":"2018-04-11T05:12:21Z","snapshot_observed_at":"2026-08-21T01:16:14.995632Z","submitted_at":"2017-07-21T18:10:26Z","title":"Learning Transferable Architectures for Scalable Image Recognition","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.07012","snapshot_observed_at":"2026-08-14T10:12:09.178082Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.178082Z"},"links":{"cited_paper":"/paper/1707.07012","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:e442d23862899d3cb9a10a008b763e802298f756d475ef9d3125531aa262438f","observation_id":"975bf528-954e-427e-b073-1d539fb4d31b","resolution":{"observed_at":"2026-08-14T10:12:09.178082Z","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":"2026-08-14T10:12:09.182655Z","title":"Caffe: Convolutional architecture for fast feature embedding","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.182655Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:82288ba3f11972ddb5ac099094a49506949dc8f05e26cf5199bd89b6a6c1b57d","observation_id":"2b4a514a-bcd6-43af-a8d7-373f17971b58","resolution":{"observed_at":"2026-08-14T10:12:09.182655Z","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":"2026-08-14T10:12:09.187302Z","title":"Srivastava, G","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.187302Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:ed061a9c20ec4fbef77113e0615e97783b95217d04503b8e905e7d5dc6beef08","observation_id":"aafc0ec2-a97c-495a-bf47-e72e528f19c8","resolution":{"observed_at":"2026-08-14T10:12:09.187302Z","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":"2026-08-14T10:12:09.191797Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.191797Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:965d7816c4915d4dbf9e7656432e64de4b2f01c48b46e6138477fe6886709d53","observation_id":"1c23a235-6a97-4b29-8f64-eba9bd45bf70","resolution":{"observed_at":"2026-08-14T10:12:09.191797Z","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":"2026-08-14T10:12:09.196659Z","title":"Hariharan, P","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.196659Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:3e7d14d69eeafe8c4752fc3f8060fccddafefae2e90f398603d5f62beef3e2c4","observation_id":"5ada2815-cc29-4149-9cae-2e266fe2c7c6","resolution":{"observed_at":"2026-08-14T10:12:09.196659Z","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":"2026-08-14T10:12:09.201236Z","title":"Imagenet classification with deep convolutional neural networks","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.201236Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:8a1efe9e0c68ca9af5bffbfdf535b73b04ef8565c6b40816f4ac428233cb186a","observation_id":"9e123e91-7910-4948-9896-43db3e9ed290","resolution":{"observed_at":"2026-08-14T10:12:09.201236Z","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":"2026-08-14T10:12:09.205983Z","title":"https://github.com/pytorch/pytorch","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.205983Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:d42ccac9e1ae9f1f67f69b0381afa15dfeb9448c7d26ba37fb3d2d29db6fb0e6","observation_id":"e19cc35f-e38f-4b96-b8a7-003fa1a55b69","resolution":{"observed_at":"2026-08-14T10:12:09.205983Z","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":"2026-08-14T10:12:09.210983Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.210983Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:15ffe4ba13f2e5fa3950a186f1d04fab24bb4c8fbfdc8f424adb05559ff00c10","observation_id":"d0c4d8e1-2d9e-4578-96ce-6c0098f2b058","resolution":{"observed_at":"2026-08-14T10:12:09.210983Z","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":"2026-08-14T10:12:09.215806Z","title":"https://github.com/pytorch/vision","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.215806Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:627faae87855ea28b67b241fd188e23f5f619d438ce98361269a71d36d26b0da","observation_id":"1fc74bc4-fae8-4c6a-bb29-ac8c9234b75b","resolution":{"observed_at":"2026-08-14T10:12:09.215806Z","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":"2026-08-14T10:12:09.220989Z","title":"Multi-scale context aggregation by dilated convolutions","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.220989Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:bdd3a328bd843d0df59d16f3ab8e60f4ae8d16f8a66df43022702874976e4ad6","observation_id":"192a6369-ea03-4db9-a58b-81f2d7c277c4","resolution":{"observed_at":"2026-08-14T10:12:09.220989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.00915","last_updated":"2017-05-12T03:25:47Z","snapshot_observed_at":"2026-08-16T22:55:31.005114Z","submitted_at":"2016-06-02T21:52:21Z","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.00915","snapshot_observed_at":"2026-08-14T10:12:09.226502Z","title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.226502Z"},"links":{"cited_paper":"/paper/1606.00915","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:89b1fe1bac8da3434b63b41823532cc6bf41d95099fe70f92743f52a8f5ec5cb","observation_id":"1e9eea44-7db8-4843-821a-10c63c0d0d40","resolution":{"observed_at":"2026-08-14T10:12:09.226502Z","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":"2026-08-14T10:12:09.231211Z","title":"Holschneider, R","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.231211Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:e8d5f1a540ca7f7efbc4f00ac56c00fa0de63290e3ff41cea21de20d73ee4cc0","observation_id":"82b0679d-d164-4b26-bccf-8d6e1eb62770","resolution":{"observed_at":"2026-08-14T10:12:09.231211Z","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":"2026-08-14T10:12:09.236412Z","title":"Semantic understanding of scenes through the ade20k dataset","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.236412Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:3dc5737a775ba62eee28a29a38c0cfdbe3dcb886035ccdb1aa21008cbe30d4df","observation_id":"c236d0b3-cec6-49d7-b4a7-398d17e3d314","resolution":{"observed_at":"2026-08-14T10:12:09.236412Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.05587","last_updated":"2017-12-05T18:06:21Z","snapshot_observed_at":"2026-08-16T09:04:18.586776Z","submitted_at":"2017-06-17T22:48:57Z","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.05587","snapshot_observed_at":"2026-08-14T10:12:09.241897Z","title":"Re- thinking atrous convolution for semantic image segmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.241897Z"},"links":{"cited_paper":"/paper/1706.05587","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:3aeff90c614aa30f7aba349a8f029f42df807dcee957e0d3cf7df40eb1f83d6c","observation_id":"6a169a50-ac62-43f7-ba84-91e67bf3f585","resolution":{"observed_at":"2026-08-14T10:12:09.241897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.02611","last_updated":"2018-08-22T20:41:10Z","snapshot_observed_at":"2026-08-19T01:37:57.013512Z","submitted_at":"2018-02-07T19:37:11Z","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.02611","snapshot_observed_at":"2026-08-14T10:12:09.246837Z","title":"Encoder-decoder with atrous separable convolution for semantic image seg- mentation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.246837Z"},"links":{"cited_paper":"/paper/1802.02611","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:87e2689be60ebd44d8fc997de82df2e5c9790dfcb6a069836a6a8fe5c5fb4e2f","observation_id":"8bdfaacc-1670-47e5-8586-69ef15206d39","resolution":{"observed_at":"2026-08-14T10:12:09.246837Z","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":"2026-08-14T10:12:09.251960Z","title":"Multi-scale context intertwining for semantic segmentation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.251960Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:b13864ce9cc0e8628caee97a4eaf4259e188522ab252cef24732fca3dd46aeca","observation_id":"ba4e52bb-0c15-4492-97d6-4eadaae7ef6e","resolution":{"observed_at":"2026-08-14T10:12:09.251960Z","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":"2026-08-14T10:12:09.257239Z","title":"Cordts, M","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.257239Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:c2af827bab68cae03fc251964f8f3f5b237ddcd54e7347aeedccba3383e95387","observation_id":"5bbb5b69-91e1-4245-8902-06be1217ab98","resolution":{"observed_at":"2026-08-14T10:12:09.257239Z","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":"2026-08-14T10:12:09.262279Z","title":"Torchcv: A pytorch-based framework for deep learning in computer vision","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.262279Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:2ef7f0c7d6ff1481fa0b01c700fbf71ff29b2429882d5a891b992dbe012ed503","observation_id":"56988afd-a10f-4e22-a159-ef7d8174e445","resolution":{"observed_at":"2026-08-14T10:12:09.262279Z","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":"2026-08-14T10:12:09.267391Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.267391Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:2171886936c3ac20ac2e6b5c7ff29709e78f6225d366aff3d40dcbca5ef095f6","observation_id":"8c0328e8-f861-4e44-8be3-4cf31a1d50ba","resolution":{"observed_at":"2026-08-14T10:12:09.267391Z","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":"2026-08-14T10:12:09.272453Z","title":"Roth, Le Lu, Amal Farag, Hoo-Chang Shin, Jiamin Liu, Evrim B","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.272453Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:e005886ee41724202cba6b0db78691b494ad41990d770cd3b5fa2a1356e056fd","observation_id":"00d07fca-f238-4f0d-a28b-d884ce96cfad","resolution":{"observed_at":"2026-08-14T10:12:09.272453Z","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":"2026-08-14T10:12:09.277453Z","title":"Roth, Jiamin Liu, Evrim Turkbey, and Ronald M","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.277453Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:d2dca13c91b73a96eade4460fe69a05b638ccbed7a7bf889fa2171a3f4b7e6c0","observation_id":"014b80e4-8ced-4b69-bce4-9f9d5540acc3","resolution":{"observed_at":"2026-08-14T10:12:09.277453Z","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":"2026-08-14T10:12:09.283121Z","title":"Training deep networks to be spatially sensitive","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.283121Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:213c753f878ab2dd267e59b20e16fd0a5abbdef4fbfa9182e8ec916234520f85","observation_id":"5b0a0cf4-bf6b-4314-a2f0-ab2220e08bb8","resolution":{"observed_at":"2026-08-14T10:12:09.283121Z","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":"2026-08-14T10:12:11.320127Z","title":"Shimoda and K","venue":null,"work_id":"fde3f218-bc53-4b8d-9b88-2b1269a78f3d","year":2016},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.288734Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:9cc1f0e07c02e233c79b4d10573b31546888734ec869894cf6b0a8ea9fae8d16","observation_id":"3877e7c1-40df-4125-b4df-3d5589ea0160","resolution":{"observed_at":"2026-08-14T10:12:11.326358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:11.300655Z","title":"Learning representations for automatic colorization","venue":null,"work_id":"7806d708-aee1-4d69-94bb-53f6381528c5","year":2016},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.293906Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:18a44484764063c95823d013badb2c3ee5fb6750509efb729d22aad20497ed26","observation_id":"3cd002b1-bcc8-4c46-b09f-29fc68c41f04","resolution":{"observed_at":"2026-08-14T10:12:11.306348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:11.282558Z","title":null,"venue":null,"work_id":"bb266280-fff9-4dcd-8130-7817adb355a6","year":2017},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.299656Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:0a1c4c432ccf4a6252b9a31a3d4163b4749787ab9e4fd68b4d4c4551c38f8cd7","observation_id":"f7678aa3-dddd-487e-8d87-9a3e7ab38fc1","resolution":{"observed_at":"2026-08-14T10:12:11.288358Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:11.263702Z","title":null,"venue":null,"work_id":"da3ee947-f82f-4e49-aba0-76e2210cc7ea","year":2017},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.304590Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:9ebaeefb40dd8701bdf4b92952857cfc385d9e7133f5f0eaf566f52fae7a63da","observation_id":"519367fe-8bf7-4d0d-8bbd-1cc1c3dd1fcd","resolution":{"observed_at":"2026-08-14T10:12:11.270063Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:11.246204Z","title":"Learning deep features for scene recognition using places database","venue":null,"work_id":"cef7c6d4-bd20-44ed-85b2-693132a46b32","year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.309774Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:2cd334860216e06f3692a0902cea62695ec07a54d97fb89cbaacd3e989006890","observation_id":"5c3e6499-4878-4cfc-b8de-418285dbdf62","resolution":{"observed_at":"2026-08-14T10:12:11.251748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:11.228987Z","title":"The pascal visual object classes challenge: A retrospective","venue":null,"work_id":"b1113b32-70a8-407f-98e5-81acbd19c764","year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.315263Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:1195813ae82fc9ec6091b29a29f4bea81ea26bc37e3592a1c8bd4928f10ee7d5","observation_id":"35500db6-100f-4cfb-a3af-2b23a7e21536","resolution":{"observed_at":"2026-08-14T10:12:11.234583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:09.320454Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.320454Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:a4681566dfa8ee560b9a2af7175cd28e8d9a6c383234a80b3084b39504bdd0f5","observation_id":"a0774d1b-960b-4007-866a-ec4ee52db6af","resolution":{"observed_at":"2026-08-14T10:12:09.320454Z","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":"2026-08-14T10:12:09.325344Z","title":"Object detectors emerge in deep scene cnns","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.325344Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:18c1fb1d74694c87cd00276ca53718b6f266e9990c50b48eb9ddb3e87f5ba99a","observation_id":"e11b381b-eec4-4940-a4e9-b9f824ff34bd","resolution":{"observed_at":"2026-08-14T10:12:09.325344Z","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":"2026-08-14T10:12:11.188539Z","title":"Is object localization for free?-weakly-supervised learning with convolutional neural networks","venue":null,"work_id":"5a406672-5237-47b1-ae76-4bed6a5e067d","year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.330156Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:1703ea220798e5b1fb24a629d14f34109a039070f11e6694ece75b6b120c302c","observation_id":"72195009-3c9c-4663-9055-1847e79ea02b","resolution":{"observed_at":"2026-08-14T10:12:11.194799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1506.02106","last_updated":"2016-07-23T17:41:43Z","snapshot_observed_at":"2026-08-15T22:52:58.522985Z","submitted_at":"2015-06-06T02:45:48Z","title":"What's the Point: Semantic Segmentation with Point Supervision","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.02106","snapshot_observed_at":"2026-08-14T10:12:09.336038Z","title":"What’s the point: Semantic segmentation with point supervision","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.336038Z"},"links":{"cited_paper":"/paper/1506.02106","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:8e3357f51862d0575b1d2969c3df32cb0d7cd0cefa5b277d6ca4318c864b2543","observation_id":"23aa3bae-6009-4a27-a7dc-1ac484460059","resolution":{"observed_at":"2026-08-14T10:12:09.336038Z","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":"2026-08-14T10:12:11.172011Z","title":"Hariharan, P","venue":null,"work_id":"7010fe91-f5e4-4884-ad39-2db46710268a","year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.342306Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:5c42c61ac510728d31caeb00f88bc65e232dd8ca90e8a7148fd51d2ba951de1f","observation_id":"49ba4d1e-e962-454e-8910-7c5c3b475e72","resolution":{"observed_at":"2026-08-14T10:12:11.176764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:11.155974Z","title":"Mostajabi, P","venue":null,"work_id":"b85e29c6-438a-4de1-bb9e-9ee54c0fd6af","year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.347576Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:9302e3be9f8240f0ef03bf9382c7a62e97836c46a6d014ef54ced29c3cb0c883","observation_id":"ba11bf6d-f27d-49a2-9b14-9e9830bdfec1","resolution":{"observed_at":"2026-08-14T10:12:11.160880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:11.139560Z","title":"Weakly- and semi-supervised learning of a deep convolutional network for semantic image segmentation","venue":null,"work_id":"646e0346-70a7-45c8-8f1f-ae566e86f776","year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.353815Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:c502abac16ce59ab86cec6a949cc3228eb4b52736f67f84026fc7f96d41cb168","observation_id":"82ca1cb7-016c-4fcd-a856-19b2f2617b99","resolution":{"observed_at":"2026-08-14T10:12:11.144971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:09.359245Z","title":"Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.359245Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:97d14f209f6dbc49ac6b39098a3c38c381532a6d191ad3ce20dea094bac6b7fd","observation_id":"28d4a5bb-5eea-4fca-a6f2-cab82aea7dc4","resolution":{"observed_at":"2026-08-14T10:12:09.359245Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1603.07485","last_updated":"2016-11-23T09:53:29Z","snapshot_observed_at":"2026-08-19T08:49:47.319885Z","submitted_at":"2016-03-24T09:04:16Z","title":"Simple Does It: Weakly Supervised Instance and Semantic Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.07485","snapshot_observed_at":"2026-08-14T10:12:09.365161Z","title":"Weakly supervised semantic labelling and instance segmentation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.365161Z"},"links":{"cited_paper":"/paper/1603.07485","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:22befff86556b5f5c3d5bb3400bdd94e6fa12b5dfec9bb497a986fcbf1b55dd0","observation_id":"049a2024-f716-4533-87c2-ef5b082df7a2","resolution":{"observed_at":"2026-08-14T10:12:09.365161Z","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":"2026-08-14T10:12:11.112293Z","title":"Constrained convolutional neural networks for weakly supervised segmentation","venue":null,"work_id":"783cb9b8-edfa-4073-911d-1460b5285bfc","year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.370293Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:49e08ed9b625e58dfb18dbdece246d3b1e4d7adf156abc4691b3cb2bf4e25bb5","observation_id":"8cbc8052-3a19-4f93-ac76-221856a34b7c","resolution":{"observed_at":"2026-08-14T10:12:11.117331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.3964","last_updated":"2016-02-02T20:55:59Z","snapshot_observed_at":"2026-08-18T18:05:55.864331Z","submitted_at":"2014-09-13T16:12:43Z","title":"Self-taught Object Localization with Deep Networks","version":7},"cited_work":{"arxiv_id":"1409.3964","doi":null,"metadata_source":"pith","pith_arxiv_id":"1409.3964","snapshot_observed_at":"2026-08-14T10:12:09.783067Z","title":"Self-taught Object Localization with Deep Networks","venue":"cs.CV","work_id":"63d132f0-d52a-4359-93ec-8162d5651c2b","year":2014},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.374757Z"},"links":{"cited_paper":"/paper/1409.3964","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:e1eb05121766645ba62a27691342c97b65c0063f40eeddc8f5a2c64b2cf17145","observation_id":"d9ba377a-b2a3-47a7-8e68-34281d006429","resolution":{"observed_at":"2026-08-14T10:12:09.788599Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.00949","last_updated":"2016-02-22T20:26:43Z","snapshot_observed_at":"2026-08-18T22:27:48.329443Z","submitted_at":"2015-03-03T14:06:02Z","title":"Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning","version":3},"cited_work":{"arxiv_id":"1503.00949","doi":null,"metadata_source":"pith","pith_arxiv_id":"1503.00949","snapshot_observed_at":"2026-08-14T10:12:09.759410Z","title":"Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning","venue":"cs.CV","work_id":"9735195a-4966-49bb-9050-dc5a7693a04c","year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.380948Z"},"links":{"cited_paper":"/paper/1503.00949","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:9ef75b8668c83cd950640b2e34ecf0f692ebfd5f79bfeddaa0753fc8cac387b4","observation_id":"e83827fb-084a-4689-8012-1a5265da0445","resolution":{"observed_at":"2026-08-14T10:12:09.765096Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:11.095200Z","title":"Schwing, and Raquel Urtasun","venue":null,"work_id":"092022c4-bdb0-47fb-9b50-09030e76755a","year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.387113Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:875e528bc46977022ab5e0cf359b21247ce1d1a9fa38cdb614bca85d90389d4a","observation_id":"334b5d27-c0be-45a3-b558-662e46240b46","resolution":{"observed_at":"2026-08-14T10:12:11.100288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:11.079580Z","title":"Fully convolu- tional multi-class multiple instance learning","venue":null,"work_id":"f2d6565b-c267-456c-a9a1-1c20c149917b","year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.391771Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:42e5df993ecc137bcd4b918aefa0035ddc6394e981276122fde4ce755adc6b2d","observation_id":"a08c45ea-66f3-47f0-8ae3-38402f241d5c","resolution":{"observed_at":"2026-08-14T10:12:11.084467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T10:12:11.063494Z","title":"From image-level to pixel-level labeling with convolutional networks","venue":null,"work_id":"36e85ae9-5537-4af8-85ff-e75ff67d8adc","year":2015},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.396560Z"},"links":{"citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:844a9c3d5f7fa27db32dc382345641fda70e8eb80ee5c29b6b2ed9c6cc8fd9d1","observation_id":"946ceaef-1d46-4655-93db-df146aad35ab","resolution":{"observed_at":"2026-08-14T10:12:11.068784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1602.04984","last_updated":"2016-03-12T08:22:30Z","snapshot_observed_at":"2026-08-14T22:10:09.789633Z","submitted_at":"2016-02-16T11:05:24Z","title":"Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.04984","snapshot_observed_at":"2026-08-14T10:12:09.401456Z","title":"Deconvolutional feature stacking for weakly- supervised semantic segmentation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-14T10:12:09.401456Z"},"links":{"cited_paper":"/paper/1602.04984","citing_paper":"/paper/1908.11820"},"observation_digest":"sha256:b804989e6b016a7e93eb844555efc71aa5b6d51e43a6c97893e0f69a0f1d14b1","observation_id":"eaf4e4bc-5f77-420d-aa8d-9f4080b25e26","resolution":{"observed_at":"2026-08-14T10:12:09.401456Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.11820","last_updated":"2019-08-30T16:18:26Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-18T00:43:50.332935Z","submitted_at":"2019-08-30T16:18:26Z","title":"Learning Rich Representations For Structured Visual Prediction Tasks"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":3,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":81,"verified_exact":4,"verified_fuzzy":12},"total_outbound_references":143},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 100 of 143 outbound references and 0 inbound Pith citation observations for arXiv:1908.11820."}