{"id":"https://openalex.org/W3011955270","doi":"https://doi.org/10.1109/apsipaasc47483.2019.9023340","title":"Part-Based Bilinear CNN For Person Re-Identification","display_name":"Part-Based Bilinear CNN For Person Re-Identification","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3011955270","doi":"https://doi.org/10.1109/apsipaasc47483.2019.9023340","mag":"3011955270"},"language":"en","primary_location":{"id":"doi:10.1109/apsipaasc47483.2019.9023340","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc47483.2019.9023340","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5100360957","display_name":"Li Li","orcid":"https://orcid.org/0000-0001-5036-0424"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li Li","raw_affiliation_strings":["Lanzhou Bocai Technology Co.,Ltd"],"affiliations":[{"raw_affiliation_string":"Lanzhou Bocai Technology Co.,Ltd","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101515499","display_name":"Jianwu Dang","orcid":"https://orcid.org/0000-0003-4504-5758"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianwu Dang","raw_affiliation_strings":["Lanzhou Bocai Technology Co.,Ltd"],"affiliations":[{"raw_affiliation_string":"Lanzhou Bocai Technology Co.,Ltd","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102836357","display_name":"Yangping Wang","orcid":"https://orcid.org/0000-0002-6226-0460"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yangping Wang","raw_affiliation_strings":["Lanzhou Bocai Technology Co.,Ltd"],"affiliations":[{"raw_affiliation_string":"Lanzhou Bocai Technology Co.,Ltd","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082259804","display_name":"Song Wang","orcid":"https://orcid.org/0000-0003-4152-5295"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song Wang","raw_affiliation_strings":["Lanzhou Bocai Technology Co.,Ltd"],"affiliations":[{"raw_affiliation_string":"Lanzhou Bocai Technology Co.,Ltd","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058638624","display_name":"Zhenhai Zhang","orcid":"https://orcid.org/0000-0002-4310-0525"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhenhai Zhang","raw_affiliation_strings":["Lanzhou Bocai Technology Co.,Ltd"],"affiliations":[{"raw_affiliation_string":"Lanzhou Bocai Technology Co.,Ltd","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100360957"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.19115392,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"10","issue":null,"first_page":"368","last_page":"374"},"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T13282","display_name":"Automated Road and Building Extraction","score":0.987500011920929,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/discriminative-model","display_name":"Discriminative model","score":0.892410397529602},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.8469687700271606},{"id":"https://openalex.org/keywords/bilinear-interpolation","display_name":"Bilinear interpolation","score":0.8452423810958862},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7364668846130371},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7148959636688232},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6903489232063293},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6505953669548035},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5847684144973755},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5305284261703491},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5101728439331055},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4940948784351349},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45738404989242554},{"id":"https://openalex.org/keywords/network-model","display_name":"Network model","score":0.4195706248283386},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.20516526699066162},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1326933205127716}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.892410397529602},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.8469687700271606},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.8452423810958862},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7364668846130371},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7148959636688232},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6903489232063293},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6505953669548035},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5847684144973755},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5305284261703491},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5101728439331055},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4940948784351349},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45738404989242554},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.4195706248283386},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.20516526699066162},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1326933205127716},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/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},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipaasc47483.2019.9023340","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc47483.2019.9023340","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1928419358","https://openalex.org/W1949591461","https://openalex.org/W1982925187","https://openalex.org/W2046835352","https://openalex.org/W2068042582","https://openalex.org/W2104657103","https://openalex.org/W2106053110","https://openalex.org/W2125889200","https://openalex.org/W2135442311","https://openalex.org/W2146897752","https://openalex.org/W2151873133","https://openalex.org/W2163352848","https://openalex.org/W2300840837","https://openalex.org/W2432402544","https://openalex.org/W2754931255","https://openalex.org/W2963066927","https://openalex.org/W2964346648","https://openalex.org/W3100555577"],"related_works":["https://openalex.org/W2950524887","https://openalex.org/W2883502031","https://openalex.org/W2261271299","https://openalex.org/W2963066927","https://openalex.org/W4280638452","https://openalex.org/W3111811104","https://openalex.org/W4380083739","https://openalex.org/W2964944724","https://openalex.org/W4285020665","https://openalex.org/W4322208588"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,8,17,35,40,43,47,51,55,61,67,87,92,99,108],"problems":[3],"of":[4,12,42,46,63,66,76,91],"image":[5,49],"misalignment":[6,44],"and":[7,54,79,102,119],"weak":[9],"discriminative":[10,72],"feature":[11,78,81],"Person":[13],"Re-Identification(Re-ID),":[14],"based":[15],"on":[16,50,98],"fine-grained":[18],"network":[19,25,30,69,94,122],"bilinear":[20,57],"CNN,":[21],"a":[22],"multipart":[23],"ReID":[24,52],"is":[26,31,96],"proposed.":[27],"The":[28,89,104],"branch":[29,68],"used":[32,59,84],"to":[33,38,70,85],"learn":[34],"part":[36,65],"features":[37],"reduce":[39],"influence":[41],"problem":[45],"datasets":[48],"effect,":[53],"compact":[56],"pooling(CPB)s":[58],"for":[60],"fusion":[62],"each":[64],"generate":[71],"feature.":[73],"Weighted":[74],"values":[75],"block":[77],"global":[80],"loss":[82],"are":[83],"optimize":[86],"network.":[88],"validity":[90],"proposed":[93,109],"structure":[95],"verified":[97],"dataset":[100],"CUHK03":[101],"Market-1501.":[103],"results":[105],"show":[106],"that":[107],"model":[110],"has":[111],"higher":[112],"average":[113],"recognition":[114],"accuracy":[115],"than":[116],"traditional":[117],"algorithms":[118],"other":[120],"similar":[121],"models.":[123]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
