{"id":"https://openalex.org/W2902710455","doi":"https://doi.org/10.1109/icmew.2018.8551570","title":"An Enhanced Deep Convolutional Neural Network for Person Re-Identification","display_name":"An Enhanced Deep Convolutional Neural Network for Person Re-Identification","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2902710455","doi":"https://doi.org/10.1109/icmew.2018.8551570","mag":"2902710455"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2018.8551570","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2018.8551570","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":"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/A5100944672","display_name":"Tiansheng Guo","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":true,"raw_author_name":"Tiansheng Guo","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057147465","display_name":"Dongfei Wang","orcid":"https://orcid.org/0000-0002-5947-736X"},"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":"Dongfei Wang","raw_affiliation_strings":["Academy of Broadcasting Science, Beijing"],"affiliations":[{"raw_affiliation_string":"Academy of Broadcasting Science, Beijing","institution_ids":["https://openalex.org/I4210111085"]}]},{"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":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, 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":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, 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":["Academy of Broadcasting Science, Beijing"],"affiliations":[{"raw_affiliation_string":"Academy of Broadcasting Science, Beijing","institution_ids":["https://openalex.org/I4210111085"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100944672"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14241882,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","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/T12740","display_name":"Gait Recognition and Analysis","score":0.9904999732971191,"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/T11448","display_name":"Face recognition and analysis","score":0.9901000261306763,"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/computer-science","display_name":"Computer science","score":0.8365129232406616},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8021519184112549},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7321233749389648},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5951706767082214},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.579625129699707},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5595342516899109},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5369123816490173},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.5332894325256348},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4658072590827942},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41588035225868225},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06506112217903137}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8365129232406616},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8021519184112549},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7321233749389648},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5951706767082214},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.579625129699707},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5595342516899109},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5369123816490173},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.5332894325256348},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4658072590827942},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41588035225868225},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06506112217903137},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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:10.1109/icmew.2018.8551570","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2018.8551570","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":37,"referenced_works":["https://openalex.org/W41482161","https://openalex.org/W1602182271","https://openalex.org/W1920259731","https://openalex.org/W1928419358","https://openalex.org/W1949591461","https://openalex.org/W1971955426","https://openalex.org/W1982925187","https://openalex.org/W2014850105","https://openalex.org/W2144172034","https://openalex.org/W2194775991","https://openalex.org/W2204750386","https://openalex.org/W2259687230","https://openalex.org/W2289130514","https://openalex.org/W2300840837","https://openalex.org/W2342611082","https://openalex.org/W2344924411","https://openalex.org/W2502225121","https://openalex.org/W2526833393","https://openalex.org/W2584637367","https://openalex.org/W2736410039","https://openalex.org/W2740442402","https://openalex.org/W2768610172","https://openalex.org/W2963438548","https://openalex.org/W2964163358","https://openalex.org/W2964289004","https://openalex.org/W2964346648","https://openalex.org/W3098057481","https://openalex.org/W3098711604","https://openalex.org/W4317524344","https://openalex.org/W6601733005","https://openalex.org/W6636160605","https://openalex.org/W6681239517","https://openalex.org/W6692470526","https://openalex.org/W6693957032","https://openalex.org/W6696128504","https://openalex.org/W6737462254","https://openalex.org/W6784740494"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W2354322770","https://openalex.org/W4237547500","https://openalex.org/W1570848052","https://openalex.org/W2373192430","https://openalex.org/W4239268388","https://openalex.org/W1537496349","https://openalex.org/W4243305035","https://openalex.org/W2379407973","https://openalex.org/W2350267540"],"abstract_inverted_index":{"Traditional":[0],"hand-crafted":[1,38,45,72],"feature":[2,8,63,80],"and":[3,40,55,74,82,94],"Convolutional":[4],"Neural":[5],"Network":[6],"(CNN)":[7],"are":[9],"two":[10],"different":[11],"types":[12],"of":[13,34,51,58,103,112,123,130],"image":[14],"descriptors":[15],"in":[16,48,86,106,115],"person":[17],"re-identification":[18],"task.":[19],"In":[20],"this":[21],"work,":[22],"we":[23],"propose":[24],"a":[25,78,87,127],"novel":[26],"network":[27],"called":[28],"Enhanced":[29],"CNN.":[30],"We":[31,90],"take":[32],"advantage":[33],"the":[35,49,56,59,100,113,121],"complementarity":[36],"between":[37],"features":[39,42,46,73,76],"CNN":[41,53,75],"by":[43],"making":[44],"participate":[47],"construction":[50],"our":[52,104,124],"model":[54,125],"generation":[57],"final":[60],"features.":[61],"A":[62],"reconstruction":[64],"block":[65],"(FRB)":[66],"is":[67],"designed":[68],"to":[69,77,98,108],"jointly":[70],"map":[71],"unitary":[79],"space":[81],"fuse":[83],"them":[84],"together":[85],"learned":[88],"way.":[89],"adopt":[91],"identification":[92],"loss":[93,96],"verification":[95],"simultaneously":[97],"supervise":[99],"training":[101],"process":[102],"model,":[105],"order":[107],"make":[109],"fully":[110],"usage":[111],"information":[114],"re-id":[116,135],"labels.":[117],"Extensive":[118],"experiments":[119],"demonstrate":[120],"superiority":[122],"over":[126],"wide":[128],"range":[129],"state-of-the-art":[131],"methods":[132],"on":[133],"four":[134],"datasets":[136],"(Market-1501,":[137],"CUHK03,":[138],"CUHKOI,":[139],"VIPeR).":[140]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}