{"id":"https://openalex.org/W4313184106","doi":"https://doi.org/10.1109/icpr56361.2022.9956524","title":"Semi-supervised Dual-Domain Adaptation for Semantic Segmentation","display_name":"Semi-supervised Dual-Domain Adaptation for Semantic Segmentation","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4313184106","doi":"https://doi.org/10.1109/icpr56361.2022.9956524"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956524","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956524","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"2022 26th International Conference on Pattern Recognition (ICPR)","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/A5027016896","display_name":"Ying Chen","orcid":"https://orcid.org/0000-0002-1914-8780"},"institutions":[{"id":"https://openalex.org/I180949307","display_name":"Illinois Institute of Technology","ror":"https://ror.org/037t3ry66","country_code":"US","type":"education","lineage":["https://openalex.org/I180949307"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ying Chen","raw_affiliation_strings":["Illinois Institute of Technology,Department of Computer Science,Chicago,IL,USA,60616"],"affiliations":[{"raw_affiliation_string":"Illinois Institute of Technology,Department of Computer Science,Chicago,IL,USA,60616","institution_ids":["https://openalex.org/I180949307"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107222587","display_name":"Xu Ouyang","orcid":"https://orcid.org/0000-0003-2852-5733"},"institutions":[{"id":"https://openalex.org/I180949307","display_name":"Illinois Institute of Technology","ror":"https://ror.org/037t3ry66","country_code":"US","type":"education","lineage":["https://openalex.org/I180949307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xu Ouyang","raw_affiliation_strings":["Illinois Institute of Technology,Department of Computer Science,Chicago,IL,USA,60616"],"affiliations":[{"raw_affiliation_string":"Illinois Institute of Technology,Department of Computer Science,Chicago,IL,USA,60616","institution_ids":["https://openalex.org/I180949307"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060295702","display_name":"Kaiyue Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I180949307","display_name":"Illinois Institute of Technology","ror":"https://ror.org/037t3ry66","country_code":"US","type":"education","lineage":["https://openalex.org/I180949307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaiyue Zhu","raw_affiliation_strings":["Illinois Institute of Technology,Department of Computer Science,Chicago,IL,USA,60616"],"affiliations":[{"raw_affiliation_string":"Illinois Institute of Technology,Department of Computer Science,Chicago,IL,USA,60616","institution_ids":["https://openalex.org/I180949307"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023698748","display_name":"Gady Agam","orcid":null},"institutions":[{"id":"https://openalex.org/I180949307","display_name":"Illinois Institute of Technology","ror":"https://ror.org/037t3ry66","country_code":"US","type":"education","lineage":["https://openalex.org/I180949307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gady Agam","raw_affiliation_strings":["Illinois Institute of Technology,Department of Computer Science,Chicago,IL,USA,60616"],"affiliations":[{"raw_affiliation_string":"Illinois Institute of Technology,Department of Computer Science,Chicago,IL,USA,60616","institution_ids":["https://openalex.org/I180949307"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5027016896"],"corresponding_institution_ids":["https://openalex.org/I180949307"],"apc_list":null,"apc_paid":null,"fwci":0.9415,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.77336089,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"230","last_page":"237"},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9943000078201294,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9907000064849854,"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":0.8359503746032715},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7092791795730591},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6393673419952393},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5319241285324097},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5195786952972412},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48094862699508667},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.480620801448822},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.48004472255706787},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.47427138686180115},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47026047110557556},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.41439032554626465}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8359503746032715},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7092791795730591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6393673419952393},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5319241285324097},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5195786952972412},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48094862699508667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.480620801448822},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.48004472255706787},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.47427138686180115},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47026047110557556},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.41439032554626465},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956524","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956524","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2118045473","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2431874326","https://openalex.org/W2487365028","https://openalex.org/W2562192638","https://openalex.org/W2765407302","https://openalex.org/W2787241931","https://openalex.org/W2798964604","https://openalex.org/W2895281799","https://openalex.org/W2953070460","https://openalex.org/W2962808524","https://openalex.org/W2962976523","https://openalex.org/W2963073217","https://openalex.org/W2963107255","https://openalex.org/W2963136578","https://openalex.org/W2963865469","https://openalex.org/W2972285644","https://openalex.org/W2976986388","https://openalex.org/W2979715488","https://openalex.org/W2981512393","https://openalex.org/W2982410491","https://openalex.org/W2985406498","https://openalex.org/W2985409929","https://openalex.org/W2986381065","https://openalex.org/W2991471181","https://openalex.org/W2992308087","https://openalex.org/W2995808743","https://openalex.org/W2997664062","https://openalex.org/W3004505403","https://openalex.org/W3017361373","https://openalex.org/W3034562924","https://openalex.org/W3034590424","https://openalex.org/W3034631418","https://openalex.org/W3035236545","https://openalex.org/W3035680157","https://openalex.org/W3045262295","https://openalex.org/W3110002552","https://openalex.org/W3110080985","https://openalex.org/W3120562181","https://openalex.org/W3122349775","https://openalex.org/W3172292202","https://openalex.org/W3175294391","https://openalex.org/W3176969075","https://openalex.org/W6677741084","https://openalex.org/W6730623217","https://openalex.org/W6733814495","https://openalex.org/W6745136726","https://openalex.org/W6746282794","https://openalex.org/W6748692255","https://openalex.org/W6750655690","https://openalex.org/W6755287564","https://openalex.org/W6761661548","https://openalex.org/W6762226699","https://openalex.org/W6765590276","https://openalex.org/W6766945662","https://openalex.org/W6767268582","https://openalex.org/W6768220425","https://openalex.org/W6769443539","https://openalex.org/W6772553744","https://openalex.org/W6773271645","https://openalex.org/W6776821665","https://openalex.org/W6780757718","https://openalex.org/W6780765913","https://openalex.org/W6788960751"],"related_works":["https://openalex.org/W4297577100","https://openalex.org/W3008176773","https://openalex.org/W3035557009","https://openalex.org/W3021676282","https://openalex.org/W2955172689","https://openalex.org/W2341113105","https://openalex.org/W3204418343","https://openalex.org/W3132602785","https://openalex.org/W2343346879","https://openalex.org/W3046182208"],"abstract_inverted_index":{"Deep":[0],"learning":[1,10,52],"approaches":[2,11,22],"for":[3,99],"semantic":[4,100,141,223],"segmentation":[5,224],"rely":[6],"primarily":[7],"on":[8,219],"supervised":[9],"and":[12,50,126,176],"require":[13],"substantial":[14],"efforts":[15],"in":[16,140,202],"producing":[17],"pixel-level":[18],"annotations.":[19],"Further,":[20],"such":[21,61],"may":[23],"perform":[24],"poorly":[25],"when":[26],"applied":[27],"to":[28,75,80,87,134,165,192,232],"unseen":[29],"image":[30],"domains.":[31],"To":[32],"cope":[33],"with":[34,42,54,85],"these":[35],"limitations,":[36],"both":[37,136],"unsupervised":[38],"domain":[39,96],"adaptation":[40,97,131,157,181],"(UDA)":[41],"full":[43],"source":[44,108,174],"supervision":[45,49,56],"but":[46],"without":[47],"target":[48,118,177,200],"semi-supervised":[51,95,129],"(SSL)":[53],"partial":[55],"have":[57],"been":[58],"proposed.":[59],"While":[60],"methods":[62,218],"are":[63,120],"effective":[64,127],"at":[65],"aligning":[66],"different":[67],"feature":[68],"distributions,":[69],"there":[70],"is":[71,146,182,190,230],"still":[72],"a":[73,103,113,124,155,185,203],"need":[74],"efficiently":[76],"exploit":[77],"unlabeled":[78,199],"data":[79,109,119,175,195,201],"address":[81,94,135],"the":[82,167,173,213,235],"performance":[83],"gap":[84],"respect":[86],"fully-supervised":[88],"methods.":[89],"In":[90],"this":[91],"paper":[92],"we":[93,153],"(SSDA)":[98],"segmentation,":[101],"where":[102],"large":[104],"amount":[105,115],"of":[106,116,148,170,237],"labeled":[107,117],"as":[110,112],"well":[111],"small":[114],"available.":[121],"We":[122,210],"propose":[123],"novel":[125],"two-step":[128],"dual-domain":[130],"(SSDDA)":[132],"approach":[133,215],"cross-and":[137],"intra-domain":[138,180],"gaps":[139],"segmentation.":[142],"The":[143],"proposed":[144,214],"framework":[145],"comprised":[147],"two":[149,220],"mixing":[150,161,198],"modules.":[151],"First,":[152],"conduct":[154],"cross-domain":[156],"via":[158],"an":[159],"image-level":[160],"strategy,":[162],"which":[163,189],"learns":[164],"align":[166],"distribution":[168],"shift":[169],"features":[171],"between":[172],"data.":[178],"Second,":[179],"achieved":[183],"using":[184],"separate":[186],"student-teacher":[187],"network":[188],"built":[191],"generate":[193],"category-level":[194],"augmentation":[196],"by":[197],"way":[204],"that":[205,212],"respects":[206],"predicted":[207],"object":[208],"boundaries.":[209],"demonstrate":[211],"outperforms":[216],"state-of-the-art":[217],"common":[221],"synthetic-to-real":[222],"benchmarks.":[225],"An":[226],"extensive":[227],"ablation":[228],"study":[229],"provided":[231],"further":[233],"validate":[234],"effectiveness":[236],"our":[238],"approach.":[239]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}