{"id":"https://openalex.org/W7098528531","doi":null,"title":"Recursive context propagation network for semantic segmentation","display_name":"Recursive context propagation network for semantic segmentation","publication_year":2016,"publication_date":"2016-09-24","ids":{"openalex":"https://openalex.org/W7098528531"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.916.4329","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.916.4329","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://papers.nips.cc/paper/5282-recursive-context-propagation-network-for-semantic-scene-labeling.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Abhishek Sharma","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abhishek Sharma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028613002","display_name":"Oncel Tuzel","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oncel Tuzel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Ming-yu Liu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming-yu Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.5099267,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9746000170707703,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9746000170707703,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.007499999832361937,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.006800000090152025,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/semantic-segmentation","display_name":"semantic segmentation","score":0.9679999947547913},{"id":"https://openalex.org/keywords/context-propagation","display_name":"context propagation","score":0.8560000061988831},{"id":"https://openalex.org/keywords/recursive-neural-networks","display_name":"recursive neural networks","score":0.8579999804496765},{"id":"https://openalex.org/keywords/bottom-up-aggregation","display_name":"bottom-up aggregation","score":0.9520000219345093},{"id":"https://openalex.org/keywords/contextual-information","display_name":"contextual information","score":0.3889999985694885},{"id":"https://openalex.org/keywords/superpixels","display_name":"superpixels","score":0.4790000021457672},{"id":"https://openalex.org/keywords/real-time-segmentation","display_name":"real-time segmentation","score":0.40299999713897705}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7885000109672546},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7421000003814697},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6043999791145325},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5831999778747559},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5537999868392944},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5516999959945679},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5382000207901001},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.5200999975204468},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.4997999966144562},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4731999933719635},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4706000089645386},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4341999888420105},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.42669999599456787},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42260000109672546},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.36809998750686646},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.34369999170303345},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.31690001487731934},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3107999861240387},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2971000075340271},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C2961294","wikidata":"https://www.wikidata.org/wiki/Q166863","display_name":"Color space","level":3,"score":0.2531000077724457}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.916.4329","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.916.4329","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://papers.nips.cc/paper/5282-recursive-context-propagation-network-for-semantic-scene-labeling.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.920.8188","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.920.8188","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2014_5282.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7100456075","https://openalex.org/W2951221812","https://openalex.org/W3026707436","https://openalex.org/W2186025005","https://openalex.org/W2606724497","https://openalex.org/W2964096383","https://openalex.org/W3002840510","https://openalex.org/W2804816177","https://openalex.org/W2124592697","https://openalex.org/W1546771929"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,15,36,41,48,56,122,128],"deep":[3],"feed-forward":[4],"neural":[5,18],"network":[6,19],"architecture":[7,20],"for":[8,21,124],"pixel-wise":[9,125],"se-mantic":[10],"scene":[11],"labeling.":[12],"It":[13,28,106],"uses":[14],"novel":[16],"recursive":[17],"context":[22],"propagation,":[23],"referred":[24],"to":[25,85],"as":[26],"rCPN.":[27],"first":[29],"maps":[30],"the":[31,52,60,67,75,81,100,140],"local":[32,45,72],"visual":[33],"features":[34],"into":[35,47],"semantic":[37],"space":[38],"followed":[39],"by":[40],"bottom-up":[42],"aggregation":[43],"of":[44,51,59,70,110,127],"information":[46,62,69,76],"global":[49],"representation":[50],"entire":[53],"image.":[54],"Then":[55],"top-down":[57],"propagation":[58],"aggregated":[61],"takes":[63,117,144],"place":[64],"that":[65,99,143],"enhances":[66],"contextual":[68],"each":[71],"feature.":[73],"Therefore,":[74],"from":[77,134],"every":[78,86],"location":[79],"in":[80],"image":[82,132],"is":[83,107],"propagated":[84],"other":[87],"location.":[88],"Experimental":[89],"results":[90],"on":[91,121],"Stanford":[92],"background":[93],"and":[94,116],"SIFT":[95],"Flow":[96],"datasets":[97],"show":[98],"proposed":[101],"method":[102],"outperforms":[103],"previous":[104,114],"ap-proaches.":[105],"also":[108],"orders":[109],"magnitude":[111],"faster":[112],"than":[113],"methods":[115],"only":[118],"0.07":[119],"seconds":[120,148],"GPU":[123],"labeling":[126],"256":[129,131],"\u00d7":[130],"starting":[133],"raw":[135],"RGB":[136],"pixel":[137],"values,":[138],"given":[139],"super-pixel":[141],"mask":[142],"an":[145,150],"additional":[146],"0.3":[147],"using":[149],"off-the-shelf":[151],"implementation.":[152],"1":[153]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2025-10-27T00:00:00"}