{"id":"https://openalex.org/W3203126248","doi":"https://doi.org/10.1109/ic-nidc54101.2021.9660560","title":"Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification","display_name":"Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification","publication_year":2021,"publication_date":"2021-11-17","ids":{"openalex":"https://openalex.org/W3203126248","doi":"https://doi.org/10.1109/ic-nidc54101.2021.9660560","mag":"3203126248"},"language":"en","primary_location":{"id":"doi:10.1109/ic-nidc54101.2021.9660560","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic-nidc54101.2021.9660560","pdf_url":null,"source":{"id":"https://openalex.org/S4363608589","display_name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","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":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","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/A5075198788","display_name":"Zheng Hu","orcid":"https://orcid.org/0000-0002-8874-5466"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Hu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021302609","display_name":"Chuang Zhu","orcid":"https://orcid.org/0000-0001-5155-7069"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuang Zhu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101760462","display_name":"Gang He","orcid":"https://orcid.org/0000-0003-4964-8871"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang He","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":3.5322,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.95101076,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"91","last_page":"95"},"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":0.9947999715805054,"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":0.9879999756813049,"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":0.8160117864608765},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7838777899742126},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.6802041530609131},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6760438680648804},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.607761800289154},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5936789512634277},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5580722689628601},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.5561069846153259},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5162860751152039},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4510110318660736},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.44266214966773987},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4409346878528595}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8160117864608765},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7838777899742126},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.6802041530609131},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6760438680648804},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.607761800289154},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5936789512634277},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5580722689628601},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.5561069846153259},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5162860751152039},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4510110318660736},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.44266214966773987},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4409346878528595},{"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ic-nidc54101.2021.9660560","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic-nidc54101.2021.9660560","pdf_url":null,"source":{"id":"https://openalex.org/S4363608589","display_name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","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":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1834021097","display_name":null,"funder_award_id":"B17007","funder_id":"https://openalex.org/F4320327912","funder_display_name":"Higher Education Discipline Innovation Project"},{"id":"https://openalex.org/G6013321364","display_name":null,"funder_award_id":"61602011","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/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W41482161","https://openalex.org/W1673310716","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2204750386","https://openalex.org/W2511556322","https://openalex.org/W2585635281","https://openalex.org/W2598634450","https://openalex.org/W2770076563","https://openalex.org/W2798991696","https://openalex.org/W2842511635","https://openalex.org/W2884366600","https://openalex.org/W2949117887","https://openalex.org/W2951292523","https://openalex.org/W2953214814","https://openalex.org/W2962859295","https://openalex.org/W2963049565","https://openalex.org/W2964271799","https://openalex.org/W2984145721","https://openalex.org/W2988852559","https://openalex.org/W2995852213","https://openalex.org/W3005680577","https://openalex.org/W3034140121","https://openalex.org/W3034607353","https://openalex.org/W3034903425","https://openalex.org/W3035402405","https://openalex.org/W3035524453","https://openalex.org/W3040470209","https://openalex.org/W3042696673","https://openalex.org/W3045296664","https://openalex.org/W3046292075","https://openalex.org/W3095467524","https://openalex.org/W3103165805","https://openalex.org/W3106858249","https://openalex.org/W3138973758","https://openalex.org/W3176033671","https://openalex.org/W3204330270","https://openalex.org/W4297808394","https://openalex.org/W6746131018","https://openalex.org/W6764266060","https://openalex.org/W6771395016","https://openalex.org/W6779510507","https://openalex.org/W6787844848","https://openalex.org/W6792400148","https://openalex.org/W6793280297"],"related_works":["https://openalex.org/W3119773509","https://openalex.org/W3174759195","https://openalex.org/W3208297503","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353","https://openalex.org/W3167013339","https://openalex.org/W4287121366","https://openalex.org/W60493759"],"abstract_inverted_index":{"Unsupervised":[0],"person":[1,86,143],"re-identification":[2],"(Re-ID)":[3],"is":[4,23,100,150],"a":[5,70,103,110],"promising":[6],"and":[7,17,136],"very":[8],"challenging":[9],"research":[10],"problem":[11],"in":[12,102],"computer":[13],"vision.":[14],"Learning":[15,75],"robust":[16],"discriminative":[18,118],"features":[19],"with":[20,81],"unlabeled":[21],"data":[22],"of":[24,52,109,141,147],"central":[25],"importance":[26],"to":[27,35,95],"Re-ID.":[28,87,144],"Recently,":[29],"more":[30,104],"attention":[31],"has":[32],"been":[33],"paid":[34],"unsupervised":[36,85,142],"Re-ID":[37,126],"algorithms":[38],"based":[39],"on":[40,122],"clustered":[41],"pseudo-label.":[42],"However,":[43],"the":[44,98,117,139],"previous":[45,133],"approaches":[46],"did":[47],"not":[48],"fully":[49],"exploit":[50],"information":[51],"hard":[53,111],"samples,":[54],"simply":[55],"using":[56],"cluster":[57,91],"centroid":[58,92],"or":[59],"all":[60],"instances":[61],"for":[62,84],"contrastive":[63,93,113],"learning.":[64],"In":[65],"this":[66],"paper,":[67],"we":[68],"propose":[69],"Hard-sample":[71],"Guided":[72],"Hybrid":[73],"Contrast":[74],"(HHCL)":[76],"approach":[77,89],"combining":[78],"cluster-level":[79],"loss":[80,83,94,114],"instance-level":[82],"Our":[88],"applies":[90],"ensure":[96],"that":[97,129],"network":[99],"updated":[101],"stable":[105],"way.":[106],"Meanwhile,":[107],"introduction":[108],"instance":[112],"further":[115],"mines":[116],"information.":[119],"Extensive":[120],"experiments":[121],"two":[123],"popular":[124],"large-scale":[125],"benchmarks":[127],"demonstrate":[128],"our":[130,148],"HHCL":[131],"outperforms":[132],"state-of-the-art":[134],"methods":[135],"significantly":[137],"improves":[138],"performance":[140],"The":[145],"code":[146],"work":[149],"available":[151],"soon":[152],"at":[153],"https://github.com/bupt-ai-cz/HHCL-ReID":[154]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":4}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}