{"id":"https://openalex.org/W7133347412","doi":"https://doi.org/10.1109/ijcb65343.2025.11411264","title":"QGait: Toward Accurate Quantization for Gait Recognition","display_name":"QGait: Toward Accurate Quantization for Gait Recognition","publication_year":2025,"publication_date":"2025-09-08","ids":{"openalex":"https://openalex.org/W7133347412","doi":"https://doi.org/10.1109/ijcb65343.2025.11411264"},"language":null,"primary_location":{"id":"doi:10.1109/ijcb65343.2025.11411264","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb65343.2025.11411264","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Joint Conference on Biometrics (IJCB)","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/A5089519900","display_name":"Senmao Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Senmao Tian","raw_affiliation_strings":["Beijing Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127941825","display_name":"Haoyu Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoyu Gao","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113229123","display_name":"Gangyi Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gangyi Hong","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087181779","display_name":"Shuyun Wang","orcid":"https://orcid.org/0000-0002-4533-1386"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shuyun Wang","raw_affiliation_strings":["The University of Queensland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Queensland","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122822461","display_name":"Jingjie Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"JingJie Wang","raw_affiliation_strings":["Beijing Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012774161","display_name":"Xin Yu","orcid":"https://orcid.org/0000-0002-2572-6815"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xin Yu","raw_affiliation_strings":["The University of Queensland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Queensland","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101833854","display_name":"Shunli Zhang","orcid":"https://orcid.org/0000-0002-7139-9974"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shunli Zhang","raw_affiliation_strings":["Beijing Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.55777368,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9954000115394592,"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":0.9954000115394592,"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/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.0005000000237487257,"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/quantization","display_name":"Quantization (signal processing)","score":0.7450000047683716},{"id":"https://openalex.org/keywords/silhouette","display_name":"Silhouette","score":0.5382999777793884},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.4377000033855438},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43160000443458557},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.38089999556541443},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3808000087738037},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3467000126838684}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.7450000047683716},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6912000179290771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.607699990272522},{"id":"https://openalex.org/C58103923","wikidata":"https://www.wikidata.org/wiki/Q2286025","display_name":"Silhouette","level":2,"score":0.5382999777793884},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3808000087738037},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35100001096725464},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2906999886035919},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcb65343.2025.11411264","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb65343.2025.11411264","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Joint Conference on Biometrics (IJCB)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2091451699","https://openalex.org/W2096733369","https://openalex.org/W2104335344","https://openalex.org/W2129215140","https://openalex.org/W2154663802","https://openalex.org/W2300242332","https://openalex.org/W2322772590","https://openalex.org/W2408717537","https://openalex.org/W2745659361","https://openalex.org/W2765328347","https://openalex.org/W2788751553","https://openalex.org/W2789834341","https://openalex.org/W2884150179","https://openalex.org/W2928560789","https://openalex.org/W2963122961","https://openalex.org/W2963301258","https://openalex.org/W3034513523","https://openalex.org/W3035400973","https://openalex.org/W3097009184","https://openalex.org/W3176946833","https://openalex.org/W3195852174","https://openalex.org/W3196547485","https://openalex.org/W3201864842","https://openalex.org/W4214564952","https://openalex.org/W4312652114","https://openalex.org/W4312739039","https://openalex.org/W4319788144","https://openalex.org/W4385489997","https://openalex.org/W4386065354","https://openalex.org/W4386065704","https://openalex.org/W4386076037","https://openalex.org/W4386076330","https://openalex.org/W4390872881","https://openalex.org/W4392271601","https://openalex.org/W4392411930","https://openalex.org/W4406650295","https://openalex.org/W7133219106"],"related_works":[],"abstract_inverted_index":{"Existing":[0],"deep":[1],"learning":[2],"methods":[3,36],"have":[4],"made":[5,202],"significant":[6,129],"progress":[7],"in":[8,120,131,138,156],"gait":[9,17,48],"recognition.":[10],"Quantization":[11],"can":[12,97],"facilitate":[13],"the":[14,65,68,75,94,116,121,132,139,144,171,175,185],"application":[15],"of":[16,67,124,135,177,187],"models":[18,27],"as":[19],"a":[20,58,107,112,128,154],"model-agnostic":[21],"general":[22],"compression":[23],"technique.":[24],"Typically,":[25],"appearance-based":[26],"binarize":[28],"inputs":[29],"into":[30],"silhouette":[31],"sequences.":[32],"However,":[33,83,119],"mainstream":[34],"quantization":[35,42],"prioritize":[37],"minimizing":[38],"task":[39],"loss":[40,155],"over":[41],"error,":[43],"which":[44,62],"is":[45,148],"detrimental":[46],"to":[47,77,143,153,169],"recognition":[49],"with":[50,179],"binarized":[51],"inputs.":[52],"To":[53],"address":[54],"this,":[55,160],"we":[56,126,161],"propose":[57,162],"differentiable":[59],"soft":[60,95,113],"quantizer,":[61],"better":[63],"simulates":[64],"gradient":[66],"round":[69],"function":[70],"during":[71,115],"backpropagation.":[72],"This":[73],"enables":[74],"network":[76,99],"learn":[78],"from":[79],"subtle":[80],"input":[81],"perturbations.":[82],"our":[84,188],"theoretical":[85],"analysis":[86],"and":[87,196],"empirical":[88],"studies":[89],"reveal":[90],"that":[91,151],"directly":[92],"applying":[93],"quantizer":[96,114],"hinder":[98],"convergence.":[100],"We":[101],"addressed":[102],"this":[103,149],"issue":[104],"by":[105],"adopting":[106],"two-stage":[108],"training":[109],"strategy,":[110],"introducing":[111],"fine-tuning":[117],"phase.":[118],"first":[122],"stage":[123],"training,":[125],"observed":[127],"change":[130,150],"output":[133],"distribution":[134],"different":[136,180],"samples":[137,178],"feature":[140],"space":[141],"compared":[142],"full-precision":[145],"network.":[146],"It":[147],"led":[152],"performance.":[157],"Based":[158],"on":[159],"an":[163],"Inter-class":[164],"Distance-guided":[165],"Calibration":[166],"(IDC)":[167],"strategy":[168],"preserve":[170],"relative":[172],"distance":[173],"between":[174],"embeddings":[176],"labels.":[181],"Extensive":[182],"experiments":[183],"validate":[184],"effectiveness":[186],"approach,":[189],"demonstrating":[190],"state-of-the-art":[191],"accuracy":[192],"across":[193],"various":[194],"settings":[195],"datasets.":[197],"The":[198],"code":[199],"will":[200],"be":[201],"publicly":[203],"available.":[204]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-04T00:00:00"}
