{"id":"https://openalex.org/W3202623927","doi":"https://doi.org/10.1109/iccv48922.2021.01598","title":"Region-aware Contrastive Learning for Semantic Segmentation","display_name":"Region-aware Contrastive Learning for Semantic Segmentation","publication_year":2021,"publication_date":"2021-10-01","ids":{"openalex":"https://openalex.org/W3202623927","doi":"https://doi.org/10.1109/iccv48922.2021.01598","mag":"3202623927"},"language":"en","primary_location":{"id":"doi:10.1109/iccv48922.2021.01598","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv48922.2021.01598","pdf_url":null,"source":{"id":"https://openalex.org/S4363607764","display_name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090180284","display_name":"Hanzhe Hu","orcid":"https://orcid.org/0000-0003-2799-2655"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hanzhe Hu","raw_affiliation_strings":["Peking University,Key Laboratory of Machine Perception (MOE), School of EECS","Zhejiang Lab","Key Laboratory of Machine Perception (MOE), School of EECS, Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University,Key Laboratory of Machine Perception (MOE), School of EECS","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Zhejiang Lab","institution_ids":["https://openalex.org/I4210123185"]},{"raw_affiliation_string":"Key Laboratory of Machine Perception (MOE), School of EECS, Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113432906","display_name":"Jinshi Cui","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinshi Cui","raw_affiliation_strings":["Peking University,Key Laboratory of Machine Perception (MOE), School of EECS","Key Laboratory of Machine Perception (MOE), School of EECS, Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University,Key Laboratory of Machine Perception (MOE), School of EECS","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Key Laboratory of Machine Perception (MOE), School of EECS, Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100406725","display_name":"Liwei Wang","orcid":"https://orcid.org/0000-0003-3264-1294"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liwei Wang","raw_affiliation_strings":["Peking University,Key Laboratory of Machine Perception (MOE), School of EECS","Key Laboratory of Machine Perception (MOE), School of EECS, Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University,Key Laboratory of Machine Perception (MOE), School of EECS","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Key Laboratory of Machine Perception (MOE), School of EECS, Peking University","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5090180284"],"corresponding_institution_ids":["https://openalex.org/I20231570","https://openalex.org/I4210123185"],"apc_list":null,"apc_paid":null,"fwci":6.6365,"has_fulltext":false,"cited_by_count":122,"citation_normalized_percentile":{"value":0.97709049,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"16271","last_page":"16281"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9994000196456909,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9932000041007996,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8396416902542114},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6346700191497803},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6007950901985168},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.5668337941169739},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5151259303092957},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.47731074690818787},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4579048156738281},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44740724563598633},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.4451184868812561},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4409661889076233},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4283910393714905},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4218558073043823},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4123288691043854},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.37960249185562134}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8396416902542114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6346700191497803},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6007950901985168},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.5668337941169739},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5151259303092957},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.47731074690818787},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4579048156738281},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44740724563598633},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.4451184868812561},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4409661889076233},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4283910393714905},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4218558073043823},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4123288691043854},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.37960249185562134},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccv48922.2021.01598","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv48922.2021.01598","pdf_url":null,"source":{"id":"https://openalex.org/S4363607764","display_name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.550000011920929,"id":"https://metadata.un.org/sdg/10"},{"display_name":"Peace, Justice and strong institutions","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":82,"referenced_works":["https://openalex.org/W343636949","https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1923697677","https://openalex.org/W2108598243","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2308529009","https://openalex.org/W2321533354","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2560023338","https://openalex.org/W2561196672","https://openalex.org/W2563705555","https://openalex.org/W2598666589","https://openalex.org/W2623546809","https://openalex.org/W2630837129","https://openalex.org/W2737258237","https://openalex.org/W2780664485","https://openalex.org/W2785325870","https://openalex.org/W2798791840","https://openalex.org/W2798991696","https://openalex.org/W2799166040","https://openalex.org/W2842511635","https://openalex.org/W2888340395","https://openalex.org/W2895340641","https://openalex.org/W2955058313","https://openalex.org/W2955813853","https://openalex.org/W2963091558","https://openalex.org/W2963318290","https://openalex.org/W2963319519","https://openalex.org/W2963727650","https://openalex.org/W2963840672","https://openalex.org/W2964309882","https://openalex.org/W2965391153","https://openalex.org/W2981689412","https://openalex.org/W2982106100","https://openalex.org/W2990500698","https://openalex.org/W2990775046","https://openalex.org/W2991062542","https://openalex.org/W2993235622","https://openalex.org/W3005680577","https://openalex.org/W3009561768","https://openalex.org/W3014641072","https://openalex.org/W3035339581","https://openalex.org/W3035524453","https://openalex.org/W3043092252","https://openalex.org/W3109196706","https://openalex.org/W3109301572","https://openalex.org/W3110674625","https://openalex.org/W3113328489","https://openalex.org/W3122412340","https://openalex.org/W3168822201","https://openalex.org/W4297808394","https://openalex.org/W4385245566","https://openalex.org/W6637373629","https://openalex.org/W6639824700","https://openalex.org/W6640295612","https://openalex.org/W6684191040","https://openalex.org/W6696085341","https://openalex.org/W6698507324","https://openalex.org/W6700872662","https://openalex.org/W6730587030","https://openalex.org/W6730845260","https://openalex.org/W6739696289","https://openalex.org/W6739901393","https://openalex.org/W6747899497","https://openalex.org/W6748481559","https://openalex.org/W6749010802","https://openalex.org/W6750703173","https://openalex.org/W6754472723","https://openalex.org/W6754713557","https://openalex.org/W6756709046","https://openalex.org/W6767071366","https://openalex.org/W6768371451","https://openalex.org/W6774314701","https://openalex.org/W6774670964","https://openalex.org/W6780408438","https://openalex.org/W6785680793","https://openalex.org/W6786187962","https://openalex.org/W6787966333"],"related_works":["https://openalex.org/W2045049461","https://openalex.org/W1978893398","https://openalex.org/W2201908702","https://openalex.org/W4381094582","https://openalex.org/W2369625323","https://openalex.org/W2364579609","https://openalex.org/W1977906818","https://openalex.org/W1522139108","https://openalex.org/W2353528968","https://openalex.org/W2377040216"],"abstract_inverted_index":{"Recent":[0],"works":[1],"have":[2],"made":[3],"great":[4],"success":[5],"in":[6,13,52,68,82,171],"semantic":[7,40,80],"segmentation":[8,81],"by":[9,65],"exploiting":[10],"contextual":[11],"information":[12],"a":[14,172],"local":[15],"or":[16],"global":[17],"manner":[18],"within":[19],"individual":[20],"image":[21],"and":[22,200,223],"supervising":[23],"the":[24,33,37,53,74,83,90,98,111,121,125,138,164,177,188],"model":[25],"with":[26],"pixel-wise":[27],"cross":[28],"entropy":[29],"loss.":[30],"However,":[31],"from":[32,100,157],"holistic":[34],"view":[35],"of":[36,92,113,129],"whole":[38,54],"dataset,":[39],"relations":[41],"not":[42],"only":[43],"exist":[44],"inside":[45],"one":[46],"single":[47],"image,":[48],"but":[49],"also":[50],"prevail":[51],"training":[55,135,178,199],"data,":[56,179],"which":[57,180],"makes":[58],"solely":[59],"considering":[60],"intra-image":[61],"correlations":[62],"insufficient.":[63],"Inspired":[64],"recent":[66],"progress":[67],"unsupervised":[69],"contrastive":[70,76,104,167],"learning,":[71],"we":[72,102,116,148],"propose":[73,149],"region-aware":[75,166],"learning":[77,105,168],"(RegionContrast)":[78],"for":[79,160,175,205],"supervised":[84],"manner.":[85],"In":[86],"order":[87],"to":[88,106,118,131,150,154],"enhance":[89],"similarity":[91],"semantically":[93],"similar":[94],"pixels":[95],"while":[96,141],"keeping":[97],"discrimination":[99],"others,":[101],"employ":[103],"realize":[107],"this":[108],"objective.":[109],"With":[110],"help":[112],"memory":[114,139,184],"bank,":[115],"explore":[117],"store":[119],"all":[120,134,176],"representative":[122],"features":[123,156],"into":[124,137],"memory.":[126],"Without":[127],"loss":[128],"generality,":[130],"efficiently":[132],"incorporate":[133],"data":[136],"bank":[140],"avoiding":[142],"taking":[143],"too":[144],"much":[145,182],"computation":[146,196],"resource,":[147],"construct":[151],"region":[152,173],"centers":[153],"represent":[155],"different":[158],"categories":[159],"every":[161],"image.":[162],"Hence,":[163],"proposed":[165,192],"is":[169],"performed":[170],"level":[174],"saves":[181],"more":[183],"than":[185],"methods":[186],"exploring":[187],"pixel-level":[189],"relations.":[190],"The":[191],"RegionContrast":[193],"brings":[194],"little":[195],"cost":[197],"during":[198],"requires":[201],"no":[202],"extra":[203],"overhead":[204],"testing.":[206],"Extensive":[207],"experiments":[208],"demonstrate":[209],"that":[210],"our":[211],"method":[212],"achieves":[213],"state-of-the-art":[214],"performance":[215],"on":[216],"three":[217],"benchmark":[218],"datasets":[219],"including":[220],"Cityscapes,":[221],"ADE20K":[222],"COCO":[224],"Stuff.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":34},{"year":2023,"cited_by_count":45},{"year":2022,"cited_by_count":22}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}