{"id":"https://openalex.org/W4402915521","doi":"https://doi.org/10.1109/icip51287.2024.10648260","title":"Gengmm: Generalized Gaussian-Mixture-Based Domain Adaptation Model for Semantic Segmentation","display_name":"Gengmm: Generalized Gaussian-Mixture-Based Domain Adaptation Model for Semantic Segmentation","publication_year":2024,"publication_date":"2024-09-27","ids":{"openalex":"https://openalex.org/W4402915521","doi":"https://doi.org/10.1109/icip51287.2024.10648260"},"language":"en","primary_location":{"id":"doi:10.1109/icip51287.2024.10648260","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip51287.2024.10648260","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","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/A5072674174","display_name":"Nazanin Moradinasab","orcid":"https://orcid.org/0000-0003-3881-8599"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]},{"id":"https://openalex.org/I4210116219","display_name":"Engineering Systems (United States)","ror":"https://ror.org/02qg60849","country_code":"US","type":"company","lineage":["https://openalex.org/I4210116219"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nazanin Moradinasab","raw_affiliation_strings":["University of Virginia,Department of Engineering Systems and Environment,Charlottesville,VA,USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia,Department of Engineering Systems and Environment,Charlottesville,VA,USA","institution_ids":["https://openalex.org/I4210116219","https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083333299","display_name":"Hassan Jafarzadeh","orcid":"https://orcid.org/0000-0001-6946-9606"},"institutions":[{"id":"https://openalex.org/I4210116219","display_name":"Engineering Systems (United States)","ror":"https://ror.org/02qg60849","country_code":"US","type":"company","lineage":["https://openalex.org/I4210116219"]},{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hassan Jafarzadeh","raw_affiliation_strings":["University of Virginia,Department of Engineering Systems and Environment,Charlottesville,VA,USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia,Department of Engineering Systems and Environment,Charlottesville,VA,USA","institution_ids":["https://openalex.org/I4210116219","https://openalex.org/I51556381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086462231","display_name":"Donald E. Brown","orcid":"https://orcid.org/0000-0002-9140-2632"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Donald E. Brown","raw_affiliation_strings":["University of Virginia,School of Data Science,Charlottesville,VA,USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia,School of Data Science,Charlottesville,VA,USA","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5072674174"],"corresponding_institution_ids":["https://openalex.org/I4210116219","https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":0.7274,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.76411346,"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":"1078","last_page":"1084"},"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":0.9107999801635742,"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":0.9107999801635742,"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/computer-science","display_name":"Computer science","score":0.7500873804092407},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.5906973481178284},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5702719688415527},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5692311525344849},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.525631844997406},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5223087668418884},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5161767601966858},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4186363220214844},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3539016842842102},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3530719578266144},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15150749683380127},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.06303209066390991}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7500873804092407},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5906973481178284},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5702719688415527},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5692311525344849},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.525631844997406},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5223087668418884},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5161767601966858},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4186363220214844},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3539016842842102},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3530719578266144},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15150749683380127},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.06303209066390991},{"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},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip51287.2024.10648260","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip51287.2024.10648260","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2340897893","https://openalex.org/W2431874326","https://openalex.org/W2487365028","https://openalex.org/W3000172657","https://openalex.org/W3109470472","https://openalex.org/W3217147624","https://openalex.org/W4311728217","https://openalex.org/W4317038436","https://openalex.org/W4319300576","https://openalex.org/W4386076346","https://openalex.org/W4386076431","https://openalex.org/W6637154279","https://openalex.org/W6733814495","https://openalex.org/W6757817989","https://openalex.org/W6842050340","https://openalex.org/W6845611785"],"related_works":["https://openalex.org/W2997567050","https://openalex.org/W1952261593","https://openalex.org/W2014494654","https://openalex.org/W1975321310","https://openalex.org/W2990323019","https://openalex.org/W3130349901","https://openalex.org/W1579833936","https://openalex.org/W2107361128","https://openalex.org/W4394775207","https://openalex.org/W1578916557"],"abstract_inverted_index":{"Domain":[0,90],"adaptive":[1],"semantic":[2],"segmentation":[3],"is":[4,42,58,67],"the":[5,55,64,108,118,127,144],"task":[6],"of":[7,146],"generating":[8],"precise":[9],"and":[10,60,83,138],"dense":[11],"predictions":[12],"for":[13,38],"an":[14],"unlabeled":[15,101],"target":[16,65,84],"domain":[17,36,122],"using":[18],"a":[19,23,51],"model":[20],"trained":[21],"on":[22,50],"labeled":[24,79],"source":[25,56,82],"domain.":[26],"While":[27],"significant":[28],"efforts":[29],"have":[30],"been":[31],"devoted":[32],"to":[33,44,87,106,134],"improving":[34],"unsupervised":[35],"adaptation":[37,123],"this":[39],"task,":[40],"it":[41],"crucial":[43],"note":[45],"that":[46,54],"many":[47],"models":[48],"rely":[49],"strong":[52],"assumption":[53],"data":[57,66,80,102,129],"entirely":[59],"accurately":[61],"labeled,":[62],"while":[63],"unlabeled.":[68],"In":[69,93],"real-world":[70],"scenarios,":[71],"however,":[72],"we":[73,96],"often":[74],"encounter":[75],"partially":[76],"or":[77,100],"noisy":[78,136],"in":[81,113,131],"domains,":[85],"referred":[86],"as":[88],"Generalized":[89,119],"Adaptation":[91],"(GDA).":[92],"such":[94],"cases,":[95],"suggest":[97],"leveraging":[98],"weak":[99,137],"from":[103],"both":[104,132],"domains":[105,133],"narrow":[107],"gap":[109],"between":[110],"them,":[111],"resulting":[112],"effective":[114],"adaptation.":[115],"We":[116],"introduce":[117],"Gaussian-mixture-based":[120],"(GenGMM)":[121],"model,":[124],"which":[125],"harnesses":[126],"underlying":[128],"distribution":[130],"refine":[135],"pseudo":[139],"labels.":[140],"The":[141],"experiments":[142],"demonstrate":[143],"effectiveness":[145],"our":[147],"approach.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-02-25T08:12:03.925757","created_date":"2025-10-10T00:00:00"}
