{"id":"https://openalex.org/W4304080361","doi":"https://doi.org/10.1145/3503161.3547897","title":"Gait Recognition in the Wild with Multi-hop Temporal Switch","display_name":"Gait Recognition in the Wild with Multi-hop Temporal Switch","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304080361","doi":"https://doi.org/10.1145/3503161.3547897"},"language":"en","primary_location":{"id":"doi:10.1145/3503161.3547897","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547897","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","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":"Proceedings of the 30th ACM International Conference on Multimedia","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/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":false,"raw_author_name":"Jinkai Zheng","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"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":["JD Explore Academy, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD Explore Academy, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024344221","display_name":"Xiaoyan Gu","orcid":"https://orcid.org/0000-0003-0673-0058"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210156404","display_name":"Institute of Information Engineering","ror":"https://ror.org/04r53se39","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyan Gu","raw_affiliation_strings":["Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048712886","display_name":"Yaoqi Sun","orcid":"https://orcid.org/0000-0001-8874-241X"},"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":"Yaoqi Sun","raw_affiliation_strings":["Hangzhou Dianzi University & Lishui Institute of Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University & Lishui Institute of Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040877128","display_name":"Chuang Gan","orcid":"https://orcid.org/0000-0003-4031-5886"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chuang Gan","raw_affiliation_strings":["MIT-IBM Watson AI Lab, Cambridge, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT-IBM Watson AI Lab, Cambridge, MA, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"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":["Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068917997","display_name":"Wu Liu","orcid":"https://orcid.org/0000-0003-1633-7575"},"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":["JD Explore Academy, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD Explore Academy, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"last","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":["Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":22.0109,"has_fulltext":false,"cited_by_count":49,"citation_normalized_percentile":{"value":0.99767561,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"6136","last_page":"6145"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":1.0,"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":1.0,"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":0.9879000186920166,"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/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.9711999893188477,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7775669097900391},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.7223178148269653},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6661619544029236},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6084815859794617},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4372696280479431},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3794347643852234},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.21562916040420532}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7775669097900391},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.7223178148269653},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6661619544029236},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6084815859794617},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4372696280479431},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3794347643852234},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.21562916040420532},{"id":"https://openalex.org/C42407357","wikidata":"https://www.wikidata.org/wiki/Q521","display_name":"Physiology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3503161.3547897","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547897","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","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":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W2018331988","https://openalex.org/W2104335344","https://openalex.org/W2104475159","https://openalex.org/W2115203491","https://openalex.org/W2126680226","https://openalex.org/W2140029174","https://openalex.org/W2322772590","https://openalex.org/W2407362091","https://openalex.org/W2510190030","https://openalex.org/W2788751553","https://openalex.org/W2960957522","https://openalex.org/W2963301258","https://openalex.org/W2963854019","https://openalex.org/W2964261250","https://openalex.org/W2977530922","https://openalex.org/W2998792609","https://openalex.org/W3006871679","https://openalex.org/W3035400973","https://openalex.org/W3092754310","https://openalex.org/W3097009184","https://openalex.org/W3106350522","https://openalex.org/W3109072956","https://openalex.org/W3125736290","https://openalex.org/W3136792391","https://openalex.org/W3158409426","https://openalex.org/W3202075110","https://openalex.org/W3203640349","https://openalex.org/W3214007456","https://openalex.org/W4206982465","https://openalex.org/W4312311094","https://openalex.org/W4312652114","https://openalex.org/W6791990631"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Existing":[0],"studies":[1],"for":[2,127],"gait":[3,16,70,91,101,137,216],"recognition":[4,17,102],"are":[5],"dominated":[6],"by":[7,139],"in-the-lab":[8,47],"scenarios.":[9],"Since":[10],"people":[11],"live":[12],"in":[13,18,72,93,157],"real-world":[14,94],"senses,":[15],"the":[19,30,33,54,65,133,155,166,170,178,199,207],"wild":[20],"is":[21,195],"a":[22,79,99,187],"more":[23,202],"practical":[24],"problem":[25],"that":[26,42,123],"has":[27],"recently":[28,55],"attracted":[29],"attention":[31],"of":[32,35,69,90,136,169,180],"community":[34],"multimedia":[36],"and":[37,114,153,221],"computer":[38],"vision.":[39],"Current":[40],"methods":[41,61,122],"obtain":[43],"state-of-the-art":[44,225],"performance":[45,212],"on":[46,53,165,213],"benchmarks":[48],"achieve":[49,86],"much":[50],"worse":[51],"accuracy":[52],"proposed":[56,196,208],"in-the-wild":[57,217],"datasets":[58],"because":[59],"these":[60],"can":[62,176],"hardly":[63],"model":[64,151,200],"varied":[66],"temporal":[67,82,88,116,128,134,204],"dynamics":[68,135],"sequences":[71,138],"unconstrained":[73],"scenes.":[74,95],"Therefore,":[75],"this":[76,143],"paper":[77],"presents":[78],"novel":[80,100],"multi-hop":[81],"switch":[83],"method":[84,175,209],"to":[85,110,197],"effective":[87],"modeling":[89],"patterns":[92],"Concretely,":[96],"we":[97],"design":[98,168],"network,":[103],"named":[104],"Multi-hop":[105],"Temporal":[106],"Switch":[107],"Network":[108],"(MTSGait),":[109],"learn":[111,201],"spatial":[112],"features":[113,117,181],"multi-scale":[115],"simultaneously.":[118],"Different":[119],"from":[120],"existing":[121],"use":[124],"3D":[125,161],"convolutions":[126],"modeling,":[129],"our":[130,174],"MTSGait":[131],"models":[132],"2D":[140,171],"convolutions.":[141],"By":[142],"means,":[144],"it":[145],"achieves":[146,210],"high":[147],"efficiency":[148],"with":[149,160,224],"fewer":[150],"parameters":[152],"reduces":[154],"difficulty":[156],"optimization":[158],"compared":[159,223],"convolution-based":[162],"models.":[163],"Based":[164],"specific":[167],"convolution":[172],"kernels,":[173],"eliminate":[177],"misalignment":[179],"among":[182],"adjacent":[183],"frames.":[184],"In":[185],"addition,":[186],"new":[188],"sampling":[189],"strategy,":[190],"i.e.,":[191,219],"non-cyclic":[192],"continuous":[193],"sampling,":[194],"make":[198],"robust":[203],"features.":[205],"Finally,":[206],"superior":[211],"two":[214],"public":[215],"datasets,":[218],"GREW":[220],"Gait3D,":[222],"methods.":[226]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}