{"id":"https://openalex.org/W2902349283","doi":"https://doi.org/10.1109/icmew.2018.8551553","title":"Person Re-Identification With A Joint Learning CNN Network And A New Global Loss Function","display_name":"Person Re-Identification With A Joint Learning CNN Network And A New Global Loss Function","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2902349283","doi":"https://doi.org/10.1109/icmew.2018.8551553","mag":"2902349283"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2018.8551553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2018.8551553","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","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/A5003168154","display_name":"Linrui Xie","orcid":null},"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":"Linrui Xie","raw_affiliation_strings":["Information and Communication engineering, Beijing University of Posts and Telecommunications, China, No.10 Xitucheng Road, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information and Communication engineering, Beijing University of Posts and Telecommunications, China, No.10 Xitucheng Road, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101588482","display_name":"Guangtao Fu","orcid":"https://orcid.org/0000-0003-1045-9125"},"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":"Guangtao Fu","raw_affiliation_strings":["Information and Communication engineering, Beijing University of Posts and Telecommunications, China, No.10 Xitucheng Road, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information and Communication engineering, Beijing University of Posts and Telecommunications, China, No.10 Xitucheng Road, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002774811","display_name":"Zhuqing Jiang","orcid":"https://orcid.org/0000-0001-6308-5708"},"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":"Zhuqing Jiang","raw_affiliation_strings":["Information and Communication engineering, Beijing University of Posts and Telecommunications, China, No.10 Xitucheng Road, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information and Communication engineering, Beijing University of Posts and Telecommunications, China, No.10 Xitucheng Road, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072795322","display_name":"Aidong Men","orcid":"https://orcid.org/0000-0001-6168-9276"},"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":"Aidong Men","raw_affiliation_strings":["Information and Communication engineering, Beijing University of Posts and Telecommunications, China, No.10 Xitucheng Road, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information and Communication engineering, Beijing University of Posts and Telecommunications, China, No.10 Xitucheng Road, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101550975","display_name":"Yun Zhou","orcid":"https://orcid.org/0000-0002-7466-4593"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Zhou","raw_affiliation_strings":["Information and Communication engineering, Academy of Broadcasting Science, Beijing, China, No.2 Street Fuxingmenwai, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information and Communication engineering, Academy of Broadcasting Science, Beijing, China, No.2 Street Fuxingmenwai, Beijing, China","institution_ids":["https://openalex.org/I4210111085"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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/T11448","display_name":"Face recognition and analysis","score":0.9962999820709229,"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":0.9941999912261963,"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":0.8065050840377808},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.6298446655273438},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.6046043634414673},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5880972146987915},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5879132747650146},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5836219787597656},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5190187692642212},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.489213228225708},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46554315090179443},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.43891647458076477},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4200219511985779},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.412004679441452},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22077032923698425}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8065050840377808},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.6298446655273438},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6046043634414673},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5880972146987915},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5879132747650146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5836219787597656},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5190187692642212},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.489213228225708},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46554315090179443},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.43891647458076477},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4200219511985779},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.412004679441452},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22077032923698425},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmew.2018.8551553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2018.8551553","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1928419358","https://openalex.org/W1971955426","https://openalex.org/W1975517671","https://openalex.org/W1976948919","https://openalex.org/W1982925187","https://openalex.org/W2109759383","https://openalex.org/W2127589108","https://openalex.org/W2135442311","https://openalex.org/W2171590421","https://openalex.org/W2204750386","https://openalex.org/W2259687230","https://openalex.org/W2300840837","https://openalex.org/W2339827301","https://openalex.org/W2342611082","https://openalex.org/W2344924411","https://openalex.org/W2467139031","https://openalex.org/W2475284720","https://openalex.org/W2497344197","https://openalex.org/W2502225121","https://openalex.org/W2531440880","https://openalex.org/W2780309588","https://openalex.org/W2963108253","https://openalex.org/W2963574614","https://openalex.org/W2963713828","https://openalex.org/W2963775347","https://openalex.org/W2964304299","https://openalex.org/W3098711604","https://openalex.org/W6638791942","https://openalex.org/W6643158107","https://openalex.org/W6692470526","https://openalex.org/W6704003556","https://openalex.org/W6723727189","https://openalex.org/W6724544503","https://openalex.org/W6728374919","https://openalex.org/W6784740494"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"In":[0,22],"training":[1,101],"deep":[2],"convolutional":[3],"neural":[4],"network":[5,102,105,146],"models":[6],"to":[7,18,41,96,132],"perform":[8],"the":[9,19,27,36,43,51,57,68,77,111,120,136,144],"task":[10],"of":[11,29,39,50,53,59,113,126],"person":[12],"re-identification,":[13],"loss":[14,32,89,94,150],"function":[15,33,90,95,151],"is":[16],"critical":[17],"final":[20,133],"performance.":[21],"this":[23,92,148],"paper,":[24],"we":[25,65,85,141],"propose":[26],"use":[28],"a":[30,81,98],"new":[31,93,149],"that":[34,143],"represent":[35],"general":[37],"distribute":[38],"data":[40,58,112,127],"reduce":[42,67],"classification":[44,74],"error.":[45,75],"By":[46],"take":[47],"in":[48,128],"count":[49],"distribution":[52],"similarity":[54,108],"measurement":[55,109],"between":[56,110],"same":[60,114,121],"label":[61,115,118],"and":[62,71,116,123],"different":[63,117],"label,":[64],"can":[66,79,106,130,152],"false":[69,72],"positive":[70],"negative":[73],"Therefore":[76],"model":[78],"gain":[80],"better":[82],"capability.":[83],"Furthermore,":[84],"combine":[86],"several":[87],"establish":[88],"with":[91,147,159],"get":[97],"proposed":[99],"jointly":[100],"model.":[103],"Our":[104],"learns":[107],"at":[119],"time":[122],"each":[124],"bit":[125],"datasets":[129],"contribute":[131],"result.":[134],"Using":[135],"Market-1501":[137],"dataset":[138],"for":[139],"experiments,":[140],"show":[142],"our":[145],"yields":[153],"very":[154],"competitive":[155],"re-id":[156],"accuracy":[157],"compared":[158],"state-of-the-arts":[160],"one.":[161]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}