{"id":"https://openalex.org/W3049063470","doi":"https://doi.org/10.1109/lsp.2020.3016528","title":"Pseudo Label Based on Multiple Clustering for Unsupervised Cross-Domain Person Re-Identification","display_name":"Pseudo Label Based on Multiple Clustering for Unsupervised Cross-Domain Person Re-Identification","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3049063470","doi":"https://doi.org/10.1109/lsp.2020.3016528","mag":"3049063470"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2020.3016528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2020.3016528","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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/A5042079195","display_name":"Shuni Chen","orcid":"https://orcid.org/0000-0003-3389-8163"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuni Chen","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3389-8163","affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028339442","display_name":"Zheyi Fan","orcid":"https://orcid.org/0000-0003-3817-7998"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheyi Fan","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3817-7998","affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112555915","display_name":"Jianyuan Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianyuan Yin","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":1.8208,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.87762136,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"27","issue":null,"first_page":"1460","last_page":"1464"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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/T10812","display_name":"Human Pose and Action Recognition","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/T12740","display_name":"Gait Recognition and Analysis","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8191720247268677},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8133325576782227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7324554324150085},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.718644917011261},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5612176060676575},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5087319016456604},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.49063435196876526},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4895969033241272},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4787256419658661},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.46595531702041626},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41088855266571045},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08534619212150574}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8191720247268677},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8133325576782227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7324554324150085},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.718644917011261},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5612176060676575},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5087319016456604},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.49063435196876526},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4895969033241272},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4787256419658661},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.46595531702041626},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41088855266571045},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08534619212150574},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2020.3016528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2020.3016528","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"No poverty","id":"https://metadata.un.org/sdg/1","score":0.6000000238418579}],"awards":[{"id":"https://openalex.org/G5630613220","display_name":null,"funder_award_id":"61701029","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8786309746","display_name":null,"funder_award_id":"L192036","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1673310716","https://openalex.org/W1982925187","https://openalex.org/W2099471712","https://openalex.org/W2194775991","https://openalex.org/W2204750386","https://openalex.org/W2584637367","https://openalex.org/W2585635281","https://openalex.org/W2598634450","https://openalex.org/W2604463754","https://openalex.org/W2724213014","https://openalex.org/W2778652957","https://openalex.org/W2907197374","https://openalex.org/W2962926870","https://openalex.org/W2963000559","https://openalex.org/W2963047834","https://openalex.org/W2963067443","https://openalex.org/W2963557071","https://openalex.org/W2963842104","https://openalex.org/W2971765950","https://openalex.org/W2982026851","https://openalex.org/W2988852559","https://openalex.org/W2996946635","https://openalex.org/W2996988779","https://openalex.org/W2999929549","https://openalex.org/W3009761962","https://openalex.org/W3022668646","https://openalex.org/W3024437012","https://openalex.org/W3034372982","https://openalex.org/W3035402405","https://openalex.org/W3100927979","https://openalex.org/W4289086653","https://openalex.org/W4320013936","https://openalex.org/W6637131181","https://openalex.org/W6735531217","https://openalex.org/W6747428963","https://openalex.org/W6757445361"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4389474468","https://openalex.org/W4300172004","https://openalex.org/W3203792196","https://openalex.org/W4321649381","https://openalex.org/W2997645659","https://openalex.org/W3180787869","https://openalex.org/W2955455867","https://openalex.org/W4295929828","https://openalex.org/W3156096827"],"abstract_inverted_index":{"Person":[0],"re-identification":[1],"(Re-ID)":[2],"has":[3,19],"achieved":[4],"great":[5],"improvement":[6],"with":[7],"the":[8,89,94,132,146],"development":[9],"of":[10,40,61,76],"deep":[11],"learning.":[12],"However,":[13],"domain":[14],"adaptation":[15],"in":[16],"unsupervised":[17,147],"Re-ID":[18,143],"always":[20],"been":[21],"a":[22,48],"challenging":[23],"task.":[24],"Most":[25],"existing":[26],"works":[27],"based":[28,51,135],"on":[29,52,113,136],"clustering":[30,63],"only":[31],"cluster":[32],"once,":[33],"which":[34,57],"may":[35],"lead":[36],"to":[37,64,131],"pseudo":[38,68],"labels":[39],"poor":[41],"quality.":[42],"In":[43,70],"this":[44],"letter,":[45],"we":[46],"propose":[47],"Pseudo":[49],"Label":[50],"Multiple":[53],"Clustering":[54],"(PLMC)":[55],"approach,":[56],"makes":[58],"full":[59],"advantage":[60],"multiple":[62],"obtain":[65],"more":[66],"robust":[67],"labels.":[69],"particular,":[71],"our":[72,126],"PLMC":[73,127],"framework":[74],"consists":[75],"two":[77,105],"stages,":[78],"namely,":[79],"global":[80,98],"training":[81,85,90,104],"stage,":[82],"and":[83,100,139],"local":[84,101],"stage.":[86],"We":[87],"adopt":[88],"strategy":[91],"that":[92,125],"combines":[93],"information":[95],"learned":[96],"from":[97],"features,":[99],"features":[102],"by":[103],"stages":[106],"alternately.":[107],"Extensive":[108],"experiments":[109],"are":[110],"carried":[111],"out":[112],"three":[114],"standard":[115],"benchmark":[116],"datasets":[117],"(e.g.,":[118],"Maket1501,":[119],"DukeMTMC-ReID,":[120],"CUHK03).":[121],"The":[122],"results":[123],"demonstrate":[124],"method":[128],"is":[129],"superior":[130],"previous":[133],"methods":[134],"single":[137],"clustering,":[138],"achieves":[140],"state-of-the-art":[141],"person":[142],"performance":[144],"under":[145],"cross-domain":[148],"setting.":[149]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
