{"id":"https://openalex.org/W4226514331","doi":"https://doi.org/10.1109/tits.2022.3164407","title":"Stepwise Domain Adaptation (SDA) for Object Detection in Autonomous Vehicles Using an Adaptive CenterNet","display_name":"Stepwise Domain Adaptation (SDA) for Object Detection in Autonomous Vehicles Using an Adaptive CenterNet","publication_year":2022,"publication_date":"2022-04-07","ids":{"openalex":"https://openalex.org/W4226514331","doi":"https://doi.org/10.1109/tits.2022.3164407"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2022.3164407","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2022.3164407","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5030291480","display_name":"Guofa Li","orcid":"https://orcid.org/0000-0002-7889-4695"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guofa Li","raw_affiliation_strings":["College of Mechatronics and Control Engineering, Institute of Human Factors and Ergonomics, Shenzhen University, Guangdong, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Mechatronics and Control Engineering, Institute of Human Factors and Ergonomics, Shenzhen University, Guangdong, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010323257","display_name":"Zefeng Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zefeng Ji","raw_affiliation_strings":["College of Mechatronics and Control Engineering, Institute of Human Factors and Ergonomics, Shenzhen University, Guangdong, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Mechatronics and Control Engineering, Institute of Human Factors and Ergonomics, Shenzhen University, Guangdong, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068850333","display_name":"Xingda Qu","orcid":"https://orcid.org/0000-0003-1764-0357"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingda Qu","raw_affiliation_strings":["College of Mechatronics and Control Engineering, Institute of Human Factors and Ergonomics, Shenzhen University, Guangdong, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Mechatronics and Control Engineering, Institute of Human Factors and Ergonomics, Shenzhen University, Guangdong, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5030291480"],"corresponding_institution_ids":["https://openalex.org/I180726961"],"apc_list":null,"apc_paid":null,"fwci":7.6454,"has_fulltext":false,"cited_by_count":78,"citation_normalized_percentile":{"value":0.98243813,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"23","issue":"10","first_page":"17729","last_page":"17743"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9983999729156494,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9954000115394592,"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":0.7904056906700134},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7657478451728821},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7375292181968689},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6982611417770386},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.5978866815567017},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5735582709312439},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5676271915435791},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5364186763763428},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47315940260887146},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43497031927108765},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11260825395584106}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7904056906700134},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7657478451728821},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7375292181968689},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6982611417770386},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.5978866815567017},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5735582709312439},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5676271915435791},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5364186763763428},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47315940260887146},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43497031927108765},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11260825395584106},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2022.3164407","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2022.3164407","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8299999833106995,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G5267107715","display_name":null,"funder_award_id":"JCYJ20190808142613246","funder_id":"https://openalex.org/F4320335803","funder_display_name":"Shenzhen Fundamental Research and Discipline Layout project"},{"id":"https://openalex.org/G5481366950","display_name":null,"funder_award_id":"20200803015912001","funder_id":"https://openalex.org/F4320335803","funder_display_name":"Shenzhen Fundamental Research and Discipline Layout project"},{"id":"https://openalex.org/G7742627185","display_name":null,"funder_award_id":"51805332","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation 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/F4320335803","display_name":"Shenzhen Fundamental Research and Discipline Layout project","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1565327149","https://openalex.org/W1861492603","https://openalex.org/W1882958252","https://openalex.org/W2104094955","https://openalex.org/W2108598243","https://openalex.org/W2187089797","https://openalex.org/W2340897893","https://openalex.org/W2748021867","https://openalex.org/W2768083292","https://openalex.org/W2793067285","https://openalex.org/W2798149494","https://openalex.org/W2889151341","https://openalex.org/W2921271497","https://openalex.org/W2921387743","https://openalex.org/W2955889502","https://openalex.org/W2962793481","https://openalex.org/W2962808524","https://openalex.org/W2962823940","https://openalex.org/W2963323244","https://openalex.org/W2963730616","https://openalex.org/W2963760351","https://openalex.org/W2963927307","https://openalex.org/W2964115968","https://openalex.org/W2965843558","https://openalex.org/W2966197049","https://openalex.org/W2968634921","https://openalex.org/W2968762770","https://openalex.org/W2969583814","https://openalex.org/W2971764038","https://openalex.org/W2974291863","https://openalex.org/W2977464309","https://openalex.org/W2979548969","https://openalex.org/W2982005186","https://openalex.org/W2982593143","https://openalex.org/W2990740643","https://openalex.org/W2991359031","https://openalex.org/W3004469560","https://openalex.org/W3005626802","https://openalex.org/W3012573144","https://openalex.org/W3016101116","https://openalex.org/W3018757597","https://openalex.org/W3087060432","https://openalex.org/W3094954011","https://openalex.org/W3101584909","https://openalex.org/W3102057471","https://openalex.org/W3103461789","https://openalex.org/W3106250896","https://openalex.org/W3112856749","https://openalex.org/W3118035304","https://openalex.org/W3121281282","https://openalex.org/W3124219615","https://openalex.org/W3126174111","https://openalex.org/W3126994406","https://openalex.org/W3129176582","https://openalex.org/W3135204376","https://openalex.org/W3135934332","https://openalex.org/W3208016204","https://openalex.org/W3211345831","https://openalex.org/W3215870255","https://openalex.org/W6633949838","https://openalex.org/W6639480849","https://openalex.org/W6746282794","https://openalex.org/W6760424586","https://openalex.org/W6776110894","https://openalex.org/W6777046832"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W3208095355","https://openalex.org/W2052507016","https://openalex.org/W2180461068","https://openalex.org/W2177370417","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W3034745255","https://openalex.org/W4254103348","https://openalex.org/W3210378990"],"abstract_inverted_index":{"In":[0,103,140,174],"recent":[1],"years,":[2],"deep":[3,52],"learning":[4,53,199],"technologies":[5,54],"for":[6,44,55,82,237],"object":[7,23,48,56,84,128,238],"detection":[8,24,38,57,85,111,129,239],"have":[9,14],"made":[10],"great":[11],"progress":[12],"and":[13,72,220,234],"powered":[15],"the":[16,27,37,45,87,93,98,118,124,132,141,146,163,167,171,175,193,212,231],"emergence":[17],"of":[18,39,47,64,89,120],"state-of-the-art":[19,232],"models":[20],"to":[21,91,115,144,155,166,178,189,230],"address":[22],"problems.":[25],"Since":[26],"domain":[28,108,133,147,160,207,241],"shift":[29,134,208,242],"can":[30],"make":[31],"detectors":[32],"unstable":[33],"or":[34],"even":[35],"crash,":[36],"cross-domain":[40,83,127],"becomes":[41],"very":[42],"important":[43],"design":[46],"detectors.":[49],"However,":[50],"traditional":[51],"always":[58],"rely":[59],"on":[60,211],"a":[61,106,157],"large":[62],"amount":[63],"reliable":[65],"ground-truth":[66],"labelling":[67],"that":[68,225],"is":[69,101,113,135,153,187,204,228,235],"laborious,":[70],"costly,":[71],"time-consuming.":[73],"Although":[74],"an":[75,149,184,197],"advanced":[76],"approach":[77],"CycleGAN":[78,90,121],"has":[79],"been":[80],"proposed":[81,114,202],"tasks,":[86],"ability":[88],"reduce":[92],"divergence":[94,125,181],"across":[95,182],"domains":[96],"at":[97,192],"feature":[99,194],"level":[100,195],"limited.":[102],"this":[104],"paper,":[105],"stepwise":[107],"adaptation":[109],"(SDA)":[110],"method":[112,203,227],"further":[116,179],"improve":[117],"performance":[119],"by":[122,161],"minimizing":[123],"in":[126,137,170,196,206,240],"tasks.":[130],"Specifically,":[131],"addressed":[136],"two":[138],"steps.":[139],"first":[142],"step,":[143,177],"bridge":[145],"gap,":[148],"unpaired":[150],"image-to-image":[151],"translator":[152],"trained":[154],"construct":[156],"fake":[158],"target":[159,172],"translating":[162],"source":[164],"images":[165],"similar":[168],"ones":[169],"domain.":[173],"second":[176],"minimize":[180],"domains,":[183],"adaptive":[185],"CenterNet":[186],"designed":[188],"align":[190],"distributions":[191],"adversarial":[198],"manner.":[200],"Our":[201],"evaluated":[205],"scenarios":[209],"based":[210],"driving":[213],"datasets":[214],"including":[215],"Cityscapes,":[216,218],"Foggy":[217],"SIM10k,":[219],"BDD100K.":[221],"The":[222],"results":[223],"show":[224],"our":[226],"superior":[229],"methods":[233],"effective":[236],"scenarios.":[243]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":25},{"year":2022,"cited_by_count":11}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
