{"id":"https://openalex.org/W3158409426","doi":"https://doi.org/10.1109/iscas51556.2021.9401218","title":"TraND: Transferable Neighborhood Discovery for Unsupervised Cross-Domain Gait Recognition","display_name":"TraND: Transferable Neighborhood Discovery for Unsupervised Cross-Domain Gait Recognition","publication_year":2021,"publication_date":"2021-04-27","ids":{"openalex":"https://openalex.org/W3158409426","doi":"https://doi.org/10.1109/iscas51556.2021.9401218","mag":"3158409426"},"language":"en","primary_location":{"id":"doi:10.1109/iscas51556.2021.9401218","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas51556.2021.9401218","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5036007592","display_name":"Jinkai Zheng","orcid":"https://orcid.org/0000-0002-7171-2668"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinkai Zheng","raw_affiliation_strings":["Automation School, Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Automation School, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030704926","display_name":"Xinchen Liu","orcid":"https://orcid.org/0000-0003-4931-8821"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinchen Liu","raw_affiliation_strings":["AI Research of JD.com, Beijing, China"],"affiliations":[{"raw_affiliation_string":"AI Research of JD.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054311881","display_name":"Chenggang Yan","orcid":"https://orcid.org/0000-0003-1204-0512"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenggang Yan","raw_affiliation_strings":["Automation School, Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Automation School, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064810678","display_name":"Jiyong Zhang","orcid":"https://orcid.org/0000-0001-9600-8477"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiyong Zhang","raw_affiliation_strings":["Automation School, Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Automation School, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100743418","display_name":"Wu Liu","orcid":"https://orcid.org/0000-0001-6259-3049"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wu Liu","raw_affiliation_strings":["AI Research of JD.com, Beijing, China"],"affiliations":[{"raw_affiliation_string":"AI Research of JD.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100363146","display_name":"Xiao\u2013Ping Zhang","orcid":"https://orcid.org/0000-0001-5241-0069"},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xiaoping Zhang","raw_affiliation_strings":["Ryerson University, Canada"],"affiliations":[{"raw_affiliation_string":"Ryerson University, Canada","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017597537","display_name":"Tao Mei","orcid":"https://orcid.org/0000-0003-2497-7732"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Mei","raw_affiliation_strings":["AI Research of JD.com, Beijing, China"],"affiliations":[{"raw_affiliation_string":"AI Research of JD.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5036007592"],"corresponding_institution_ids":["https://openalex.org/I50760025"],"apc_list":null,"apc_paid":null,"fwci":0.5911,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.62180278,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9872000217437744,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.963100016117096,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7797525525093079},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7111001014709473},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.6567707061767578},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5612775087356567},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5185106992721558},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5107855796813965},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.4962061047554016},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.44720402359962463},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.43383505940437317},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.43164151906967163},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.41295087337493896},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08741480112075806}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7797525525093079},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7111001014709473},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.6567707061767578},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5612775087356567},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5185106992721558},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5107855796813965},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.4962061047554016},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.44720402359962463},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.43383505940437317},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.43164151906967163},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.41295087337493896},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08741480112075806},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C42407357","wikidata":"https://www.wikidata.org/wiki/Q521","display_name":"Physiology","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas51556.2021.9401218","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas51556.2021.9401218","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"display_name":"No poverty","id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W2018331988","https://openalex.org/W2035725550","https://openalex.org/W2096733369","https://openalex.org/W2104335344","https://openalex.org/W2126680226","https://openalex.org/W2133956100","https://openalex.org/W2204750386","https://openalex.org/W2296073425","https://openalex.org/W2322772590","https://openalex.org/W2441160157","https://openalex.org/W2510190030","https://openalex.org/W2519904008","https://openalex.org/W2745659361","https://openalex.org/W2756012011","https://openalex.org/W2786808285","https://openalex.org/W2798991696","https://openalex.org/W2803696107","https://openalex.org/W2896515397","https://openalex.org/W2915184863","https://openalex.org/W2922420408","https://openalex.org/W2941964676","https://openalex.org/W2948069880","https://openalex.org/W2949024437","https://openalex.org/W2951541198","https://openalex.org/W2953461088","https://openalex.org/W2962859295","https://openalex.org/W2963000559","https://openalex.org/W2963301258","https://openalex.org/W2963854019","https://openalex.org/W2963975998","https://openalex.org/W2988852559","https://openalex.org/W3000471755","https://openalex.org/W3006871679","https://openalex.org/W3035537506","https://openalex.org/W3099206234","https://openalex.org/W3111278072","https://openalex.org/W3116546446","https://openalex.org/W6675575696","https://openalex.org/W6726373078","https://openalex.org/W6761903662","https://openalex.org/W6779836534"],"related_works":["https://openalex.org/W3196155444","https://openalex.org/W4287665842","https://openalex.org/W3209574120","https://openalex.org/W3087576162","https://openalex.org/W3210156800","https://openalex.org/W3095538999","https://openalex.org/W3046775127","https://openalex.org/W3123344745","https://openalex.org/W4306321456","https://openalex.org/W2942455380"],"abstract_inverted_index":{"Gait,":[0],"i.e.,":[1,198],"the":[2,61,97,122,140,147,152,185,191],"movement":[3],"pattern":[4],"of":[5,16,143,162],"human":[6],"limbs":[7],"during":[8],"locomotion,":[9],"is":[10,156],"a":[11,31,46,49,69,89,116,127,172],"promising":[12],"biometrie":[13],"for":[14,100,110],"identification":[15],"persons.":[17],"Despite":[18],"significant":[19],"improvement":[20],"in":[21,126,146],"gait":[22,40,103,111],"recognition":[23,41],"with":[24],"deep":[25],"learning,":[26],"existing":[27],"studies":[28],"still":[29],"neglect":[30],"more":[32],"practical":[33],"but":[34],"challenging":[35],"scenario":[36],"-":[37],"unsupervised":[38,101],"cross-domain":[39,102],"which":[42,178],"aims":[43],"to":[44,55,60,76,95,137,158,184],"learn":[45,106],"model":[47,70],"on":[48,72,121,165,194],"labeled":[50,123],"dataset":[51,75],"then":[52],"adapt":[53],"it":[54],"an":[56,133],"unlabeled":[57,144],"dataset.":[58],"Due":[59],"domain":[62,98],"shift":[63],"and":[64,200],"class":[65,153],"gap,":[66],"directly":[67],"applying":[68],"trained":[71,120],"one":[73],"source":[74,124],"other":[77],"target":[78,186],"datasets":[79],"usually":[80],"obtains":[81],"very":[82],"poor":[83],"results.":[84],"Therefore,":[85],"this":[86],"paper":[87],"proposes":[88],"Transferable":[90],"Neighborhood":[91],"Discovery":[92],"(TraND)":[93],"framework":[94],"bridge":[96],"gap":[99],"recognition.":[104],"To":[105],"effective":[107],"prior":[108,182],"knowledge":[109,183],"representation,":[112],"we":[113,131,170],"first":[114],"adopt":[115],"backbone":[117],"network":[118],"pre-":[119],"data":[125],"supervised":[128],"manner.":[129],"Then":[130],"design":[132],"end-to-end":[134],"trainable":[135],"approach":[136],"automatically":[138],"discover":[139],"confident":[141,160],"neighborhoods":[142,161],"samples":[145,163],"latent":[148],"space.":[149],"During":[150],"training,":[151],"consistency":[154],"indicator":[155],"adopted":[157],"select":[159],"based":[164],"their":[166],"entropy":[167],"measurements.":[168],"Moreover,":[169],"explore":[171],"high-":[173],"entropy-first":[174],"neighbor":[175],"selection":[176],"strategy,":[177],"can":[179],"effectively":[180],"transfer":[181],"domain.":[187],"Our":[188],"method":[189],"achieves":[190],"state-of-the-art":[192],"results":[193],"two":[195],"public":[196],"datasets,":[197],"CASIA-B":[199],"OU-LP.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-05T09:29:38.588285","created_date":"2025-10-10T00:00:00"}