{"id":"https://openalex.org/W4312964852","doi":"https://doi.org/10.1109/iros47612.2022.9981567","title":"Bilateral Knowledge Distillation for Unsupervised Domain Adaptation of Semantic Segmentation","display_name":"Bilateral Knowledge Distillation for Unsupervised Domain Adaptation of Semantic Segmentation","publication_year":2022,"publication_date":"2022-10-23","ids":{"openalex":"https://openalex.org/W4312964852","doi":"https://doi.org/10.1109/iros47612.2022.9981567"},"language":"en","primary_location":{"id":"doi:10.1109/iros47612.2022.9981567","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros47612.2022.9981567","pdf_url":null,"source":{"id":"https://openalex.org/S4363607704","display_name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5007302951","display_name":"Yunnan Wang","orcid":"https://orcid.org/0000-0002-1957-2696"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunnan Wang","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Automation,China","Department of Automation, Shanghai Jiao Tong University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Automation,China","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Department of Automation, Shanghai Jiao Tong University, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083135905","display_name":"Jianxun Li","orcid":"https://orcid.org/0000-0002-6347-6677"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxun Li","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Automation,China","Department of Automation, Shanghai Jiao Tong University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Automation,China","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Department of Automation, Shanghai Jiao Tong University, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4153,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.59973496,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"10177","last_page":"10184"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9876999855041504,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9817000031471252,"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":0.7684339880943298},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7503763437271118},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.6716842651367188},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.6307792663574219},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5975097417831421},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5885769128799438},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4847259521484375},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48244649171829224},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4771503806114197},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43836042284965515},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3955477476119995},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3357159197330475},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1636963188648224}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7684339880943298},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7503763437271118},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.6716842651367188},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.6307792663574219},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5975097417831421},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5885769128799438},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4847259521484375},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48244649171829224},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4771503806114197},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43836042284965515},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3955477476119995},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3357159197330475},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1636963188648224},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros47612.2022.9981567","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros47612.2022.9981567","pdf_url":null,"source":{"id":"https://openalex.org/S4363607704","display_name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6368830644","display_name":null,"funder_award_id":"61673265","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8533081567","display_name":null,"funder_award_id":"2020YFC1512203","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1821462560","https://openalex.org/W1903029394","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2407386500","https://openalex.org/W2412782625","https://openalex.org/W2431874326","https://openalex.org/W2487365028","https://openalex.org/W2562192638","https://openalex.org/W2895281799","https://openalex.org/W2948210185","https://openalex.org/W2954054736","https://openalex.org/W2963073217","https://openalex.org/W2963107255","https://openalex.org/W2963516811","https://openalex.org/W2963958441","https://openalex.org/W2972285644","https://openalex.org/W2981512393","https://openalex.org/W3009914867","https://openalex.org/W3034756453","https://openalex.org/W3034870355","https://openalex.org/W3035294798","https://openalex.org/W3089098255","https://openalex.org/W3103855482","https://openalex.org/W3105676814","https://openalex.org/W3108566666","https://openalex.org/W3119635706","https://openalex.org/W3121634539","https://openalex.org/W3131093608","https://openalex.org/W3154061064","https://openalex.org/W3157967435","https://openalex.org/W3172292202","https://openalex.org/W3175294391","https://openalex.org/W3175308890","https://openalex.org/W3176969075","https://openalex.org/W3180003570","https://openalex.org/W3187458216","https://openalex.org/W3194859544","https://openalex.org/W4320013936","https://openalex.org/W6730623217","https://openalex.org/W6789011712"],"related_works":["https://openalex.org/W4389474468","https://openalex.org/W4300172004","https://openalex.org/W2990774877","https://openalex.org/W4321649381","https://openalex.org/W2997645659","https://openalex.org/W3180787869","https://openalex.org/W3203792196","https://openalex.org/W2955455867","https://openalex.org/W4295929828","https://openalex.org/W3093339210"],"abstract_inverted_index":{"Unsupervised":[0],"domain":[1,13,44,52],"adaptation":[2],"(UDA)":[3],"aims":[4],"to":[5,30,54,99,121,163],"learn":[6],"domain-invariant":[7],"representations":[8],"between":[9],"the":[10,15,42,50,89,101,108,111,123,128,133,138,144,148,172],"labeled":[11],"source":[12,32,43,104,112],"and":[14,27,34,45,61,194],"unlabeled":[16,173],"target":[17,35,51,145,174],"domain.":[18,90,113,146,175],"Existing":[19],"self-":[20],"training-based":[21],"UDA":[22,77],"methods":[23,184],"use":[24],"ground":[25],"truth":[26],"pseudo-":[28,46],"labels":[29,102],"supervise":[31],"data":[33,36],"respectively.":[37],"However,":[38],"strong":[39],"supervision":[40,109],"in":[41,49,78,110,127,143,151,171],"label":[47],"noise":[48,142],"lead":[53],"some":[55],"problems,":[56],"such":[57],"as":[58],"biased":[59],"predictions":[60],"over-fitting.":[62],"To":[63],"tackle":[64],"these":[65],"issues,":[66],"we":[67,92,154],"propose":[68,156],"a":[69,95,115],"novel":[70],"Bilateral":[71],"Knowledge":[72],"Distillation":[73,97,117],"(BKD)":[74],"framework":[75,180],"for":[76],"semantic":[79,152],"segmentation,":[80,153],"which":[81,106,136],"adopts":[82],"different":[83],"knowledge":[84,126],"distillation":[85],"strategies":[86],"depending":[87],"on":[88,190],"Specifically,":[91],"first":[93],"introduce":[94],"Source-Flow":[96],"(SD)":[98],"smooth":[100],"of":[103,140,178],"images,":[105],"weakens":[107],"Meanwhile,":[114],"Target-Flow":[116],"(TD)":[118],"is":[119,185],"designed":[120],"extract":[122],"inter-":[124],"class":[125,149,169],"probability":[129],"map":[130],"output":[131],"from":[132],"teacher":[134],"model,":[135],"alleviates":[137],"influence":[139],"pseudo-label":[141],"Considering":[147],"imbalance":[150],"further":[155],"an":[157],"Image-Wise":[158],"Hard":[159],"Pixel":[160],"Mining":[161],"(HPM)":[162],"address":[164],"this":[165],"issue":[166],"without":[167],"estimating":[168],"frequency":[170],"The":[176],"effectiveness":[177],"our":[179],"against":[181],"existing":[182],"state-of-the-art":[183],"demonstrated":[186],"by":[187],"extensive":[188],"experiments":[189],"two":[191],"benchmarks:":[192],"GTA5-to-Cityscapes":[193],"SYNTHIA-to-Cityscapes.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}