{"id":"https://openalex.org/W2978112156","doi":"https://doi.org/10.1109/ijcnn.2019.8852125","title":"Viewpoint-robust Person Re-identification via Deep Residual Equivariant Mapping and Fine-grained Features","display_name":"Viewpoint-robust Person Re-identification via Deep Residual Equivariant Mapping and Fine-grained Features","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978112156","doi":"https://doi.org/10.1109/ijcnn.2019.8852125","mag":"2978112156"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852125","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852125","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5003769188","display_name":"Liang Yang","orcid":"https://orcid.org/0000-0001-6291-4359"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Yang","raw_affiliation_strings":["School of Computer, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029691902","display_name":"Xiao\u2010Yuan Jing","orcid":"https://orcid.org/0000-0002-0392-8475"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao-Yuan Jing","raw_affiliation_strings":["College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090204290","display_name":"He Fulin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fulin He","raw_affiliation_strings":["YunKang Corporation, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"YunKang Corporation, Changsha, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020522229","display_name":"Fei Ma","orcid":"https://orcid.org/0000-0002-5472-4763"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Ma","raw_affiliation_strings":["School of Computer, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100753413","display_name":"Li Cheng","orcid":"https://orcid.org/0000-0003-3261-3533"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Cheng","raw_affiliation_strings":["School of Computer, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":0.10900552,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"7","issue":null,"first_page":"1","last_page":"10"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9969000220298767,"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":0.9952999949455261,"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/viewpoints","display_name":"Viewpoints","score":0.9049903154373169},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7101467847824097},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6948169469833374},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.687325119972229},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.685451865196228},{"id":"https://openalex.org/keywords/equivariant-map","display_name":"Equivariant map","score":0.6169781684875488},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6102162003517151},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5754305124282837},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.528421938419342},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.494811475276947},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.49242672324180603},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44107669591903687},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.422454833984375},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2671530544757843},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22445762157440186}],"concepts":[{"id":"https://openalex.org/C2776035091","wikidata":"https://www.wikidata.org/wiki/Q7928819","display_name":"Viewpoints","level":2,"score":0.9049903154373169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7101467847824097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6948169469833374},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.687325119972229},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.685451865196228},{"id":"https://openalex.org/C171036898","wikidata":"https://www.wikidata.org/wiki/Q256355","display_name":"Equivariant map","level":2,"score":0.6169781684875488},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6102162003517151},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5754305124282837},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.528421938419342},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.494811475276947},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.49242672324180603},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44107669591903687},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.422454833984375},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2671530544757843},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22445762157440186},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852125","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852125","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"mag:3034235275","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902235626806128","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W603908379","https://openalex.org/W1142012885","https://openalex.org/W1912570122","https://openalex.org/W1928419358","https://openalex.org/W1949591461","https://openalex.org/W1982925187","https://openalex.org/W2068042582","https://openalex.org/W2097117768","https://openalex.org/W2099471712","https://openalex.org/W2106053110","https://openalex.org/W2108598243","https://openalex.org/W2144935315","https://openalex.org/W2151103935","https://openalex.org/W2163352848","https://openalex.org/W2194775991","https://openalex.org/W2204750386","https://openalex.org/W2300840837","https://openalex.org/W2308869522","https://openalex.org/W2463071499","https://openalex.org/W2465670578","https://openalex.org/W2475284720","https://openalex.org/W2499554887","https://openalex.org/W2511791013","https://openalex.org/W2584637367","https://openalex.org/W2608797168","https://openalex.org/W2791424146","https://openalex.org/W2792373886","https://openalex.org/W2795758732","https://openalex.org/W2798385569","https://openalex.org/W2798429327","https://openalex.org/W2798775284","https://openalex.org/W2798794112","https://openalex.org/W2883348239","https://openalex.org/W2883638665","https://openalex.org/W2893770612","https://openalex.org/W2962926870","https://openalex.org/W2963289251","https://openalex.org/W2963322158","https://openalex.org/W2963842104","https://openalex.org/W2963861381","https://openalex.org/W2964044605","https://openalex.org/W2964304299","https://openalex.org/W3100506510","https://openalex.org/W3100587600","https://openalex.org/W4320013936","https://openalex.org/W6618372016","https://openalex.org/W6627095619","https://openalex.org/W6675751002","https://openalex.org/W6680962578","https://openalex.org/W6698448323","https://openalex.org/W6719241496","https://openalex.org/W6725558437","https://openalex.org/W6746003717","https://openalex.org/W6747428963"],"related_works":["https://openalex.org/W2385368906","https://openalex.org/W2902924992","https://openalex.org/W2626642044","https://openalex.org/W2619807045","https://openalex.org/W2388758053","https://openalex.org/W93537448","https://openalex.org/W2949734191","https://openalex.org/W2017333877","https://openalex.org/W2048332520","https://openalex.org/W4233821346"],"abstract_inverted_index":{"Existing":[0],"person":[1,10,67,91,160],"re-identification":[2],"methods":[3],"usually":[4],"directly":[5,30],"calculate":[6],"the":[7,85,151,169,172],"similarities":[8],"of":[9,13,80,89,139,171],"pictures":[11],"regardless":[12],"their":[14],"viewpoints.":[15],"However,":[16],"matching":[17],"persons":[18],"under":[19,92],"different":[20,78,93,132],"viewpoints":[21,79,94],"is":[22,26,35,137],"difficult":[23],"since":[24],"it":[25],"intrinsically":[27],"hard":[28],"to":[29,38,105,157],"learn":[31],"a":[32,54,81,90],"representation":[33,87,107],"which":[34],"geometrically":[36],"invariant":[37],"large":[39],"viewpoint":[40,48],"variations.":[41],"In":[42],"this":[43],"paper,":[44],"we":[45],"explicitly":[46],"take":[47],"information":[49,136],"into":[50],"account":[51],"and":[52,60,83,150],"propose":[53],"novel":[55],"Deep":[56],"Residual":[57],"Equivariant":[58],"Mapping":[59],"Fine-grained":[61],"Features":[62],"(DREMFF)":[63],"approach":[64],"for":[65,122],"viewpoint-robust":[66,159],"re-identification.":[68,161],"Specifically,":[69],"DREMFF":[70,118],"hypothesizes":[71],"that":[72],"there":[73],"exists":[74],"inherent":[75],"mapping":[76,100],"between":[77],"person,":[82],"consequently,":[84],"global":[86,148],"discrepancy":[88],"will":[95],"be":[96],"bridged":[97],"through":[98],"equivariant":[99],"by":[101],"adaptively":[102],"adding":[103],"residuals":[104],"original":[106],"according":[108],"corresponding":[109],"angle":[110],"deviation.":[111],"What's":[112],"more,":[113],"based":[114],"on":[115,163],"attention":[116],"mechanism,":[117],"extracts":[119],"fine-grained":[120,153],"features":[121,149,154],"each":[123],"image":[124],"from":[125],"multiple":[126],"salient":[127],"regions":[128],"as":[129,131],"well":[130],"scales.":[133],"These":[134],"captured":[135],"capable":[138],"providing":[140],"assistant":[141],"decision-making":[142],"at":[143],"lower":[144],"granularities.":[145],"The":[146],"mapped":[147],"learned":[152],"work":[155],"collaboratively":[156],"enable":[158],"Experiments":[162],"three":[164],"challenging":[165],"benchmarks":[166],"consistently":[167],"demonstrate":[168],"effectiveness":[170],"proposed":[173],"approach.":[174]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
