{"id":"https://openalex.org/W3032023307","doi":"https://doi.org/10.1145/3383972.3383993","title":"Improving Person Re-identification by Mask Guiding and Part Pooling","display_name":"Improving Person Re-identification by Mask Guiding and Part Pooling","publication_year":2020,"publication_date":"2020-02-15","ids":{"openalex":"https://openalex.org/W3032023307","doi":"https://doi.org/10.1145/3383972.3383993","mag":"3032023307"},"language":"en","primary_location":{"id":"doi:10.1145/3383972.3383993","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3383972.3383993","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 12th International Conference on Machine Learning and Computing","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/A5100392856","display_name":"Bin Zhang","orcid":"https://orcid.org/0000-0002-6286-6227"},"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":"Bin Zhang","raw_affiliation_strings":["School of Electronic Information Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100653133","display_name":"Yanfeng Li","orcid":"https://orcid.org/0000-0002-8441-7721"},"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":"Yanfeng Li","raw_affiliation_strings":["School of Electronic Information Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073025681","display_name":"Houjin Chen","orcid":"https://orcid.org/0000-0002-9247-8495"},"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":"Houjin Chen","raw_affiliation_strings":["School of Electronic Information Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057502807","display_name":"Jia Sun","orcid":"https://orcid.org/0000-0002-9188-7000"},"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":"Jia Sun","raw_affiliation_strings":["School of Electronic Information Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"301","last_page":"306"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9958000183105469,"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":0.9939000010490417,"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/pooling","display_name":"Pooling","score":0.8850553035736084},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8222765326499939},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7728137373924255},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5763498544692993},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5570253729820251},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5361872315406799},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5156346559524536},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4689238667488098},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4531330466270447},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.4454348385334015},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43187853693962097},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4255315661430359},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3309110999107361},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.22197574377059937},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21900585293769836}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.8850553035736084},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8222765326499939},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7728137373924255},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5763498544692993},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5570253729820251},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5361872315406799},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5156346559524536},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4689238667488098},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4531330466270447},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.4454348385334015},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43187853693962097},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4255315661430359},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3309110999107361},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.22197574377059937},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21900585293769836},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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:10.1145/3383972.3383993","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3383972.3383993","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 12th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6047840620","display_name":null,"funder_award_id":"61872030,61571036","funder_id":"https://openalex.org/F4320327720","funder_display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320327720","display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W1949591461","https://openalex.org/W1982925187","https://openalex.org/W2194775991","https://openalex.org/W2204750386","https://openalex.org/W2768610172","https://openalex.org/W2883779225","https://openalex.org/W2964289004","https://openalex.org/W4200399832","https://openalex.org/W4234552385","https://openalex.org/W4301409532","https://openalex.org/W6600062020","https://openalex.org/W6600100092","https://openalex.org/W6600547436","https://openalex.org/W6600585467","https://openalex.org/W6600595061","https://openalex.org/W6600708310","https://openalex.org/W6600883950","https://openalex.org/W6601022194","https://openalex.org/W6601065604","https://openalex.org/W6605721860","https://openalex.org/W6609741587"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W2559156603","https://openalex.org/W4300832495","https://openalex.org/W2987852271","https://openalex.org/W4287394948","https://openalex.org/W2967990525","https://openalex.org/W2949066288","https://openalex.org/W3119356360","https://openalex.org/W3202075396","https://openalex.org/W4214604401"],"abstract_inverted_index":{"Person":[0],"re-identification":[1,45],"(re-ID)":[2],"is":[3,47,58,84,98,114],"a":[4,43,61,80,94,131],"promising":[5],"computer":[6],"vision":[7],"task.":[8],"State-of-the-art":[9],"methods":[10],"mainly":[11],"utilize":[12],"deep":[13],"learning":[14],"based":[15],"approaches":[16],"to":[17,26,100,139],"learn":[18],"visual":[19],"features":[20,103],"for":[21],"describing":[22],"person":[23,44,56,90,129,182],"appearances.":[24],"Due":[25],"occlusion,":[27],"complex":[28],"background,":[29,79],"different":[30],"postures":[31],"and":[32,52,122,176],"light":[33],"intensity,":[34],"the":[35,76,87,105,112,117,128,141,144,154,158,162,166,172],"technology":[36],"faces":[37],"many":[38],"challenges.":[39],"In":[40],"this":[41],"paper,":[42],"method":[46,148],"proposed":[48,145],"combining":[49,64],"mask":[50,57,81,88,174],"guiding":[51,82,175],"part":[53,95,177],"pooling.":[54],"First,":[55],"generated":[59],"by":[60],"segmentation":[62],"sub-net,":[63],"Macro-Micro":[65],"Adversarial":[66],"Network":[67],"(MMAN)":[68],"with":[69,89],"Fully":[70],"Convolution":[71],"Networks":[72],"(FCN).":[73],"To":[74],"alleviate":[75],"influence":[77],"of":[78,104,111,119,143,151],"strategy":[83,97],"designed":[85,173],"integrating":[86],"feature":[91],"map.":[92],"Then":[93],"pooling":[96,178],"employed":[99,138],"extract":[101],"local":[102,123],"person.":[106],"The":[107],"final":[108],"loss":[109,121],"function":[110],"network":[113],"defined":[115],"as":[116],"combination":[118],"global":[120],"loss,":[124],"which":[125],"can":[126,180],"describe":[127],"in":[130],"comprehensive":[132],"manner.":[133],"Four":[134],"public":[135],"datasets":[136],"are":[137],"test":[140],"performance":[142],"method.":[146],"Our":[147],"achieves":[149],"rank-1/mAP":[150],"89.05%/72.83%":[152],"on":[153,157,161,165],"Market-1501,":[155],"79.03%/62.06%":[156],"DukeMTMC-reID,":[159],"46.79%/29.61%":[160],"MSMT-17,":[163],"49.50%/45.03%":[164],"CUHK03-NP.":[167],"Experimental":[168],"results":[169],"show":[170],"that":[171],"strategies":[179],"improve":[181],"re-ID":[183],"performance.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
