{"id":"https://openalex.org/W4389666860","doi":"https://doi.org/10.1109/iros55552.2023.10341878","title":"Disentangled Discriminator for Unsupervised Domain Adaptation on Object Detection","display_name":"Disentangled Discriminator for Unsupervised Domain Adaptation on Object Detection","publication_year":2023,"publication_date":"2023-10-01","ids":{"openalex":"https://openalex.org/W4389666860","doi":"https://doi.org/10.1109/iros55552.2023.10341878"},"language":"en","primary_location":{"id":"doi:10.1109/iros55552.2023.10341878","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros55552.2023.10341878","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5082413938","display_name":"Yangguang Zhu","orcid":"https://orcid.org/0000-0001-7598-4575"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangguang Zhu","raw_affiliation_strings":["University of Chinese Academy of Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100950541","display_name":"Ping Guo","orcid":"https://orcid.org/0009-0004-7222-0907"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ping Guo","raw_affiliation_strings":["Intel Labs China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intel Labs China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071032466","display_name":"Haoran Wei","orcid":"https://orcid.org/0000-0003-2283-1895"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoran Wei","raw_affiliation_strings":["University of Chinese Academy of Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100547641","display_name":"Xin Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Zhao","raw_affiliation_strings":["University of Chinese Academy of Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100943218","display_name":"Wu Xiangbin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiangbin Wu","raw_affiliation_strings":["Intel Labs China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intel Labs China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5685","last_page":"5691"},"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":0.9997000098228455,"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":0.9997000098228455,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.968255877494812},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7878634929656982},{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.7282552719116211},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6706122756004333},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5296950936317444},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.49924588203430176},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.47551876306533813},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4475376307964325},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.41595935821533203},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41205042600631714},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.2061116099357605},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08683010935783386}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.968255877494812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7878634929656982},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.7282552719116211},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6706122756004333},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5296950936317444},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.49924588203430176},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.47551876306533813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4475376307964325},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.41595935821533203},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41205042600631714},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.2061116099357605},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08683010935783386},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/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/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros55552.2023.10341878","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros55552.2023.10341878","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.8100000023841858,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2102605133","https://openalex.org/W2193145675","https://openalex.org/W2340897893","https://openalex.org/W2570343428","https://openalex.org/W2748021867","https://openalex.org/W2953070460","https://openalex.org/W2963037989","https://openalex.org/W2963351448","https://openalex.org/W2963730616","https://openalex.org/W2964115968","https://openalex.org/W2968634921","https://openalex.org/W2979548969","https://openalex.org/W2998216033","https://openalex.org/W3010381534","https://openalex.org/W3034779842","https://openalex.org/W3034937575","https://openalex.org/W3035175896","https://openalex.org/W3035673985","https://openalex.org/W3106250896","https://openalex.org/W3107610277","https://openalex.org/W3112288498","https://openalex.org/W3121281282","https://openalex.org/W3129242782","https://openalex.org/W3163536451","https://openalex.org/W3166409449","https://openalex.org/W3174017534","https://openalex.org/W3180426564","https://openalex.org/W3204359931","https://openalex.org/W4285414338","https://openalex.org/W4293518336","https://openalex.org/W4312819586","https://openalex.org/W4312993742","https://openalex.org/W6620707391","https://openalex.org/W6639480849","https://openalex.org/W6733814495","https://openalex.org/W6789505266","https://openalex.org/W6838694358"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W4380714744","https://openalex.org/W2387995142","https://openalex.org/W4319453655","https://openalex.org/W2089959425","https://openalex.org/W2964074194","https://openalex.org/W2057775761","https://openalex.org/W2985313479"],"abstract_inverted_index":{"Object":[0],"detection":[1,18,46],"plays":[2],"an":[3],"important":[4],"role":[5],"in":[6,29,49],"computer":[7],"vision":[8],"tasks":[9],"such":[10],"as":[11],"autonomous":[12],"driving,":[13],"robotics,":[14],"etc.":[15],"Typically,":[16],"a":[17,67,71,95,116,125,142],"model":[19],"is":[20,150,207],"firstly":[21],"trained":[22],"on":[23,189,203],"collected":[24],"data":[25,86,111,136,140],"and":[26,39,102,109,137],"then":[27],"deployed":[28],"real":[30,51],"world.":[31,52],"However,":[32,73],"the":[33,45,50,55,64,78,84,100,107,121,131,146,153,159,179,184,192,195,211],"discrepancy":[34,181],"exists":[35],"between":[36],"training":[37],"(source)":[38],"testing":[40],"(target)":[41],"data,":[42],"which":[43,82,206],"degrades":[44],"model's":[47],"performance":[48],"To":[53],"mitigate":[54],"negative":[56],"effects,":[57],"Unsupervised":[58],"Domain":[59],"Adaptation":[60],"(UDA)":[61],"methods":[62],"learn":[63],"features":[65],"of":[66,87,155,171,194],"shared":[68],"domain":[69,180],"via":[70,158],"discriminator.":[72,186],"existing":[74],"discriminators":[75],"consider":[76,99],"only":[77],"in-distribution":[79,101],"adversarial":[80],"learning,":[81],"ignore":[83],"out-of-distribution":[85],"individual":[88],"domains.":[89],"In":[90],"this":[91],"paper,":[92],"we":[93,199],"propose":[94],"disentangled":[96,122,185],"discriminator":[97,123],"to":[98,183],"outliers":[103],"separately.":[104],"It":[105],"aligns":[106],"source":[108,135],"target":[110,139],"with":[112,124,152],"split":[113],"branches":[114],"under":[115,141],"gated":[117],"strategy.":[118],"We":[119],"combine":[120],"Teacher-Student":[126],"(T-S)":[127],"framework":[128],"that":[129,149],"trains":[130],"student":[132,156],"using":[133],"labeled":[134],"unlabeled":[138,167],"self-training":[143],"mechanism.":[144],"Specifically,":[145,198],"teacher":[147],"network,":[148],"updated":[151],"parameters":[154],"network":[157],"exponential":[160],"moving":[161],"average,":[162],"predicts":[163],"pseudo":[164,172],"labels":[165,173],"for":[166],"data.":[168],"The":[169],"quality":[170],"can":[174],"be":[175],"improved":[176],"after":[177],"alleviating":[178],"thanks":[182],"Extensive":[187],"experiments":[188],"benchmarks":[190],"demonstrate":[191],"superiority":[193],"proposed":[196],"method.":[197],"achieve":[200],"53.9%":[201],"mAP":[202],"Foggy":[204],"Cityscapes,":[205],"7.2%":[208],"higher":[209],"than":[210],"Oracle.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}