{"id":"https://openalex.org/W3206437864","doi":null,"title":"Domain Adaptive Semantic Segmentation with Regional Contrastive Consistency Regularization.","display_name":"Domain Adaptive Semantic Segmentation with Regional Contrastive Consistency Regularization.","publication_year":2021,"publication_date":"2021-10-11","ids":{"openalex":"https://openalex.org/W3206437864","mag":"3206437864"},"language":"en","primary_location":{"id":"mag:3206437864","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2110.05170v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2110.05170v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031210225","display_name":"Qianyu Zhou","orcid":"https://orcid.org/0000-0002-5331-050X","observed_orcids":["https://orcid.org/0000-0002-5331-050X","https://orcid.org/0000-0002-6967-077X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qianyu Zhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029086540","display_name":"Chuyun Zhuang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chuyun Zhuang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019288783","display_name":"Xuequan Lu","orcid":"https://orcid.org/0000-0003-0959-408X","observed_orcids":["https://orcid.org/0000-0003-0959-408X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuequan Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5084218062","display_name":"Lizhuang Ma","orcid":"https://orcid.org/0000-0003-1653-4341","observed_orcids":["https://orcid.org/0000-0003-1653-4341"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lizhuang Ma","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-1653-4341","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":1.0,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0,"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/unsupervised-domain-adaptation","display_name":"unsupervised domain adaptation","score":0.9990000128746033},{"id":"https://openalex.org/keywords/memory-bank","display_name":"memory bank","score":0.7149999737739563},{"id":"https://openalex.org/keywords/outdoor-scene-understanding","display_name":"outdoor scene understanding","score":0.6970000267028809},{"id":"https://openalex.org/keywords/domain-shift","display_name":"domain shift","score":0.4869999885559082},{"id":"https://openalex.org/keywords/synthetic-to-real-transfer","display_name":"synthetic-to-real transfer","score":0.4620000123977661}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7595622539520264},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.725570559501648},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6136221289634705},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5427588820457458},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5284041166305542},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5154374837875366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4567365348339081},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.44220975041389465},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42371416091918945},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3236815333366394},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20230939984321594},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3206437864","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2110.05170v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3206437864","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2110.05170v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W1882958252","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2335728318","https://openalex.org/W2395611524","https://openalex.org/W2412782625","https://openalex.org/W2431874326","https://openalex.org/W2592691248","https://openalex.org/W2756073160","https://openalex.org/W2767657961","https://openalex.org/W2767722847","https://openalex.org/W2798991696","https://openalex.org/W2895281799","https://openalex.org/W2901038558","https://openalex.org/W2902727564","https://openalex.org/W2903619461","https://openalex.org/W2905535599","https://openalex.org/W2910424828","https://openalex.org/W2916332638","https://openalex.org/W2929754429","https://openalex.org/W2932414082","https://openalex.org/W2940156133","https://openalex.org/W2940597502","https://openalex.org/W2949650786","https://openalex.org/W2949907962","https://openalex.org/W2952596663","https://openalex.org/W2952787450","https://openalex.org/W2953139137","https://openalex.org/W2963120918","https://openalex.org/W2963136578","https://openalex.org/W2964309882","https://openalex.org/W2969893028","https://openalex.org/W2979509742","https://openalex.org/W2979548969","https://openalex.org/W2998607115","https://openalex.org/W2999216888","https://openalex.org/W3005680577","https://openalex.org/W3013561810","https://openalex.org/W3017324723","https://openalex.org/W3034272105","https://openalex.org/W3034417116","https://openalex.org/W3035236545","https://openalex.org/W3035294798","https://openalex.org/W3043044432","https://openalex.org/W3082576756","https://openalex.org/W3091785623","https://openalex.org/W3108566666","https://openalex.org/W3122412340","https://openalex.org/W3134134173","https://openalex.org/W3136850628","https://openalex.org/W3153264591","https://openalex.org/W3157124592","https://openalex.org/W3157653192","https://openalex.org/W3163336135","https://openalex.org/W3164607503","https://openalex.org/W3166409449","https://openalex.org/W3172507542","https://openalex.org/W3172615411","https://openalex.org/W3172949220","https://openalex.org/W3173206925","https://openalex.org/W3176748778","https://openalex.org/W3188258165","https://openalex.org/W3193426425","https://openalex.org/W4295788754","https://openalex.org/W4297808394","https://openalex.org/W4319987583"],"related_works":["https://openalex.org/W4286906661","https://openalex.org/W3017324723","https://openalex.org/W4410394827","https://openalex.org/W3110486195","https://openalex.org/W4320036972","https://openalex.org/W4404313165","https://openalex.org/W4293999382","https://openalex.org/W4312885164","https://openalex.org/W4380261215","https://openalex.org/W4312454920"],"abstract_inverted_index":{"Unsupervised":[0],"domain":[1,8,14,75],"adaptation":[2],"(UDA)":[3],"aims":[4],"to":[5,49,83,97,113,145,157,177,181],"bridge":[6],"the":[7,11,16,25,33,37,56,85,91,103,106,110,123,127,132,188],"shift":[9],"between":[10,36],"labeled":[12],"source":[13],"and":[15,43,63,100,161,179],"unlabeled":[17],"target":[18],"domain.":[19],"However,":[20],"most":[21],"existing":[22],"works":[23],"perform":[24],"global-level":[26],"feature":[27],"alignment":[28],"for":[29,74],"semantic":[30,77],"segmentation,":[31],"while":[32],"local":[34],"consistency":[35,71],"regions":[38],"has":[39],"been":[40],"largely":[41],"neglected,":[42],"these":[44],"methods":[45],"are":[46],"less":[47],"robust":[48,160],"changing":[50],"of":[51,94,109,131],"outdoor":[52],"environments.":[53,167],"Motivated":[54],"by":[55],"above":[57],"facts,":[58],"we":[59,135],"propose":[60,118],"a":[61,137,151],"novel":[62],"fully":[64],"end-to-end":[65],"trainable":[66],"approach,":[67],"called":[68],"regional":[69,87,148],"contrastive":[70,139],"regularization":[72],"(RCCR)":[73],"adaptive":[76],"segmentation.":[78],"Our":[79],"core":[80],"idea":[81],"is":[82,126,155],"pull":[84],"similar":[86],"features":[88,104,164],"extracted":[89],"from":[90,105],"same":[92],"location":[93],"different":[95,107],"images":[96,112],"be":[98,114],"closer,":[99],"meanwhile":[101],"push":[102],"locations":[108],"two":[111,142,171],"separated.":[115],"We":[116],"innovatively":[117],"momentum":[119],"projector":[120,125],"heads,":[121],"where":[122],"teacher":[124],"exponential":[128],"moving":[129],"average":[130],"student.":[133],"Besides,":[134],"present":[136],"region-wise":[138,163],"loss":[140],"with":[141],"sampling":[143],"strategies":[144],"realize":[146],"effective":[147],"consistency.":[149],"Finally,":[150],"memory":[152],"bank":[153],"mechanism":[154],"designed":[156],"learn":[158],"more":[159],"stable":[162],"under":[165],"varying":[166],"Extensive":[168],"experiments":[169],"on":[170],"common":[172],"UDA":[173],"benchmarks,":[174],"i.e.,":[175],"GTAV":[176],"Cityscapes":[178],"SYNTHIA":[180],"Cityscapes,":[182],"demonstrate":[183],"that":[184],"our":[185],"approach":[186],"outperforms":[187],"state-of-the-art":[189],"methods.":[190]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-10-25T00:00:00"}