{"id":"https://openalex.org/W3176224381","doi":"https://doi.org/10.1109/lsp.2021.3090258","title":"Multi-View Label Prediction for Unsupervised Learning Person Re-Identification","display_name":"Multi-View Label Prediction for Unsupervised Learning Person Re-Identification","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3176224381","doi":"https://doi.org/10.1109/lsp.2021.3090258","mag":"3176224381"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2021.3090258","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3090258","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":true,"oa_status":"green","oa_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/73784","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068052198","display_name":"Qingze Yin","orcid":"https://orcid.org/0000-0001-7882-7559"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qingze Yin","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058662281","display_name":"Guan\u2019an Wang","orcid":"https://orcid.org/0000-0001-6015-494X"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guan'an Wang","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210094879","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024687250","display_name":"Guodong Ding","orcid":"https://orcid.org/0000-0001-6080-5220"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guodong Ding","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039302902","display_name":"Shaogang Gong","orcid":"https://orcid.org/0000-0001-8156-2299"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shaogang Gong","raw_affiliation_strings":["Queen Mary University of London, London, U.K"],"affiliations":[{"raw_affiliation_string":"Queen Mary University of London, London, U.K","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100735663","display_name":"Zhenmin Tang","orcid":"https://orcid.org/0000-0001-6708-2205"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenmin Tang","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5068052198"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":1.8408,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.87415308,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"28","issue":null,"first_page":"1390","last_page":"1394"},"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":0.9925000071525574,"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/T11019","display_name":"Image Enhancement Techniques","score":0.987500011920929,"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":0.7772389650344849},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7560125589370728},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7291085720062256},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6214383840560913},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.6043042540550232},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6016152501106262},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5906057953834534},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.5529124736785889},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5337477922439575},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4773721396923065},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.45360785722732544},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3961961567401886},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15371474623680115}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7772389650344849},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7560125589370728},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7291085720062256},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6214383840560913},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.6043042540550232},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6016152501106262},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5906057953834534},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.5529124736785889},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5337477922439575},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4773721396923065},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.45360785722732544},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3961961567401886},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15371474623680115},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lsp.2021.3090258","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3090258","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"},{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/73784","is_oa":true,"landing_page_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/73784","pdf_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/73784","source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/73784","is_oa":true,"landing_page_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/73784","pdf_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/73784","source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.4300000071525574}],"awards":[{"id":"https://openalex.org/G3017058420","display_name":null,"funder_award_id":"202006840102","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"}],"funders":[{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3176224381.pdf","grobid_xml":"https://content.openalex.org/works/W3176224381.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W1949591461","https://openalex.org/W1966371059","https://openalex.org/W1979260620","https://openalex.org/W2046835352","https://openalex.org/W2194775991","https://openalex.org/W2204750386","https://openalex.org/W2327827989","https://openalex.org/W2397917844","https://openalex.org/W2441160157","https://openalex.org/W2516580127","https://openalex.org/W2520433280","https://openalex.org/W2520774990","https://openalex.org/W2585635281","https://openalex.org/W2770076563","https://openalex.org/W2778652957","https://openalex.org/W2799185441","https://openalex.org/W2832876791","https://openalex.org/W2891440844","https://openalex.org/W2895589658","https://openalex.org/W2898047322","https://openalex.org/W2901946163","https://openalex.org/W2904427185","https://openalex.org/W2940865854","https://openalex.org/W2948562462","https://openalex.org/W2962859295","https://openalex.org/W2962877362","https://openalex.org/W2963067443","https://openalex.org/W2963152148","https://openalex.org/W2963557071","https://openalex.org/W2963574614","https://openalex.org/W2963968597","https://openalex.org/W2963975998","https://openalex.org/W2963989829","https://openalex.org/W2964438507","https://openalex.org/W2966669298","https://openalex.org/W2981030579","https://openalex.org/W2982026851","https://openalex.org/W2988852559","https://openalex.org/W3011820993","https://openalex.org/W3022591699","https://openalex.org/W3024437012","https://openalex.org/W3035539956","https://openalex.org/W3098711604","https://openalex.org/W6726392171","https://openalex.org/W6726946684","https://openalex.org/W6726951882","https://openalex.org/W6746131018","https://openalex.org/W6754783249","https://openalex.org/W6754906984","https://openalex.org/W6755701201","https://openalex.org/W6763662452","https://openalex.org/W6774688414","https://openalex.org/W6776610661","https://openalex.org/W6784740494"],"related_works":["https://openalex.org/W4256429076","https://openalex.org/W1971174658","https://openalex.org/W2099195351","https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2348092930","https://openalex.org/W2370917603","https://openalex.org/W2012063272","https://openalex.org/W2952760143","https://openalex.org/W3129272755"],"abstract_inverted_index":{"Person":[0],"re-identification":[1],"(ReID)":[2],"aims":[3],"to":[4,47,88,128],"match":[5],"pedestrian":[6],"images":[7,42],"across":[8],"disjoint":[9],"cameras.":[10],"Existing":[11],"supervised":[12],"ReID":[13,32,53],"methods":[14],"utilize":[15],"deep":[16],"networks":[17],"and":[18,35,43,64,76,104,171],"train":[19],"them":[20],"with":[21,55],"identity-labeled":[22],"images,":[23,113],"which":[24,91],"suffer":[25],"from":[26,70,124],"limited":[27],"annotations.":[28],"Recently,":[29],"clustering-based":[30],"unsupervised":[31],"attracts":[33],"more":[34,36],"attention.":[37],"It":[38,119],"first":[39],"clusters":[40],"unlabeled":[41],"assigns":[44],"cluster":[45],"index":[46],"the":[48,56,60,80,99,129,142,161],"pseudo-identity-labels,":[49,85],"then":[50],"trains":[51],"a":[52,136,148,165],"model":[54],"pseudo-identity-labels.":[57],"However,":[58],"considering":[59],"slight":[61],"inter-class":[62],"variations":[63],"significant":[65],"intra-class":[66],"variations,":[67],"pseudo-identity-labels":[68],"learned":[69],"clustering":[71,84],"algorithms":[72],"are":[73],"usually":[74,108],"noisy":[75,116],"coarse.":[77],"To":[78],"alleviate":[79],"problems":[81],"above,":[82],"besides":[83],"we":[86],"propose":[87],"learn":[89],"pseudo-patch-labels,":[90],"brings":[92],"two":[93],"advantages:":[94],"(1)":[95],"Patch":[96],"naturally":[97],"alleviates":[98],"effect":[100],"of":[101,139,151],"backgrounds,":[102],"occlusions,":[103],"carryings":[105],"since":[106],"they":[107],"occupy":[109],"small":[110],"parts":[111],"in":[112],"thus":[114],"overcome":[115],"labels.":[117],"(2)":[118],"is":[120],"plausible":[121],"that":[122],"patches":[123],"different":[125],"pedestrians":[126,134],"belong":[127],"same":[130,143],"pseudo-identity-label.":[131],"For":[132],"example,":[133],"have":[135],"high":[137],"probability":[138],"wearing":[140,152],"either":[141],"shoes":[144],"or":[145],"pants":[146],"but":[147],"low":[149],"possibility":[150],"both.":[153],"The":[154],"experiments":[155],"demonstrate":[156],"our":[157],"proposed":[158],"method":[159],"achieves":[160],"best":[162],"performance":[163],"by":[164],"large":[166],"margin":[167],"on":[168],"both":[169],"image-":[170],"video-based":[172],"datasets.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}