{"id":"https://openalex.org/W2978712363","doi":"https://doi.org/10.1109/ijcnn.2019.8852283","title":"Adversarially Erased Learning for Person Re-identification by Fully Convolutional Networks","display_name":"Adversarially Erased Learning for Person Re-identification by Fully Convolutional Networks","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978712363","doi":"https://doi.org/10.1109/ijcnn.2019.8852283","mag":"2978712363"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852283","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852283","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":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.ulster.ac.uk/en/publications/bf9d4f42-7d69-48aa-8b4b-ab4490a5371f","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042115943","display_name":"Shuangwei Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuangwei Liu","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University of China, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University of China, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038287028","display_name":"Yunzhou Zhang","orcid":"https://orcid.org/0000-0003-0610-3732"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunzhou Zhang","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University of China, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University of China, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100326344","display_name":"Lin Qi","orcid":"https://orcid.org/0000-0002-1579-2333"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Qi","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University of China, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University of China, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070227862","display_name":"Sonya Coleman","orcid":"https://orcid.org/0000-0002-4676-7640"},"institutions":[{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sonya Coleman","raw_affiliation_strings":["Intelligent Systems Research Centre, University of Ulster, Derry, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Intelligent Systems Research Centre, University of Ulster, Derry, United Kingdom","institution_ids":["https://openalex.org/I138801177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015138346","display_name":"Dermot Kerr","orcid":"https://orcid.org/0000-0002-5077-0658"},"institutions":[{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Dermot Kerr","raw_affiliation_strings":["Intelligent Systems Research Centre, University of Ulster, Derry, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Intelligent Systems Research Centre, University of Ulster, Derry, United Kingdom","institution_ids":["https://openalex.org/I138801177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076895776","display_name":"Shangdong Zhu","orcid":"https://orcid.org/0000-0003-4701-2028"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shangdong Zhu","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University of China, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University of China, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5042115943"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":0.1022,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45089785,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"39","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9965999722480774,"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":0.9912999868392944,"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/discriminative-model","display_name":"Discriminative model","score":0.9061103463172913},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7960729598999023},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7294130325317383},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6969174146652222},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6369884610176086},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6197661757469177},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5171265602111816},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.47801077365875244},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4211188852787018},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3405376672744751}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9061103463172913},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7960729598999023},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7294130325317383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6969174146652222},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6369884610176086},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6197661757469177},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5171265602111816},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.47801077365875244},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4211188852787018},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3405376672744751},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852283","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852283","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":"pmh:oai:pure.atira.dk:openaire/bf9d4f42-7d69-48aa-8b4b-ab4490a5371f","is_oa":true,"landing_page_url":"https://pure.ulster.ac.uk/en/publications/bf9d4f42-7d69-48aa-8b4b-ab4490a5371f","pdf_url":null,"source":{"id":"https://openalex.org/S4306402454","display_name":"Ulster University Research Portal (Ulster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138801177","host_organization_name":"University of Ulster","host_organization_lineage":["https://openalex.org/I138801177"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Liu, S, Zhang, Y, Qi, L, Coleman, S, Kerr, D & Zhu, S 2019, Adversarially Erased Learning for Person Re-identification by Fully Convolutional Networks. in 2019 International Joint Conference on Neural Networks, IJCNN 2019. vol. 2019-July, 8852283, IEEE, Budapest. https://doi.org/10.1109/IJCNN.2019.8852283","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:pure.atira.dk:publications/bf9d4f42-7d69-48aa-8b4b-ab4490a5371f","is_oa":true,"landing_page_url":"http://www.scopus.com/inward/record.url?scp=85073246923&partnerID=8YFLogxK","pdf_url":null,"source":{"id":"https://openalex.org/S4306402454","display_name":"Ulster University Research Portal (Ulster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138801177","host_organization_name":"University of Ulster","host_organization_lineage":["https://openalex.org/I138801177"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Liu , S , Zhang , Y , Qi , L , Coleman , S , Kerr , D & Zhu , S 2019 , Adversarially Erased Learning for Person Re-identification by Fully Convolutional Networks . in 2019 International Joint Conference on Neural Networks, IJCNN 2019 . vol. 2019-July , 8852283 , IEEE , Budapest . https://doi.org/10.1109/IJCNN.2019.8852283","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:openaire/bf9d4f42-7d69-48aa-8b4b-ab4490a5371f","is_oa":true,"landing_page_url":"https://pure.ulster.ac.uk/en/publications/bf9d4f42-7d69-48aa-8b4b-ab4490a5371f","pdf_url":null,"source":{"id":"https://openalex.org/S4306402454","display_name":"Ulster University Research Portal (Ulster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138801177","host_organization_name":"University of Ulster","host_organization_lineage":["https://openalex.org/I138801177"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Liu, S, Zhang, Y, Qi, L, Coleman, S, Kerr, D & Zhu, S 2019, Adversarially Erased Learning for Person Re-identification by Fully Convolutional Networks. in 2019 International Joint Conference on Neural Networks, IJCNN 2019. vol. 2019-July, 8852283, IEEE, Budapest. https://doi.org/10.1109/IJCNN.2019.8852283","raw_type":"contributionToPeriodical"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1949591461","https://openalex.org/W1982925187","https://openalex.org/W2099471712","https://openalex.org/W2108598243","https://openalex.org/W2120419212","https://openalex.org/W2194775991","https://openalex.org/W2204750386","https://openalex.org/W2289130514","https://openalex.org/W2295107390","https://openalex.org/W2300840837","https://openalex.org/W2395611524","https://openalex.org/W2531440880","https://openalex.org/W2584637367","https://openalex.org/W2585635281","https://openalex.org/W2598634450","https://openalex.org/W2747685395","https://openalex.org/W2752782242","https://openalex.org/W2768610172","https://openalex.org/W2770739811","https://openalex.org/W2798376494","https://openalex.org/W2798590501","https://openalex.org/W2883554151","https://openalex.org/W2898074480","https://openalex.org/W2955058313","https://openalex.org/W2962926870","https://openalex.org/W2963091558","https://openalex.org/W2963289251","https://openalex.org/W2963322158","https://openalex.org/W2963420686","https://openalex.org/W2963910742","https://openalex.org/W2964130064","https://openalex.org/W2964274719","https://openalex.org/W2964304299","https://openalex.org/W2964337551","https://openalex.org/W2998508940","https://openalex.org/W4320013936","https://openalex.org/W6640054144","https://openalex.org/W6728374919","https://openalex.org/W6733146167","https://openalex.org/W6735531217","https://openalex.org/W6743440100","https://openalex.org/W6743731764","https://openalex.org/W6746003717","https://openalex.org/W6746316091","https://openalex.org/W6753200639","https://openalex.org/W6754852571","https://openalex.org/W6755780448"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W1487808658","https://openalex.org/W3119773509","https://openalex.org/W3208297503","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353","https://openalex.org/W4309346246"],"abstract_inverted_index":{"The":[0],"generalization":[1],"ability":[2],"of":[3,51,72,175,195],"deep":[4],"person":[5,12],"re-identification":[6],"networks":[7],"is":[8,170],"subject":[9],"to":[10,40,47,60,63,94,124,143,172,179],"inadequate":[11],"data":[13],"and":[14,44,117,140,150,162,182],"occlusions.":[15],"To":[16,75],"relieve":[17],"this":[18,159],"dilemma,":[19],"we":[20,36,54,82,130],"propose":[21,83],"a":[22,38,84,120,132],"feature-level":[23],"augmentation":[24],"strategy,":[25],"Adversarially":[26],"Erased":[27],"Learning":[28],"Module":[29,135],"(AELM),":[30],"using":[31],"two":[32],"adversarial":[33],"classifiers.":[34],"Specifically,":[35],"utilize":[37],"classifier":[39,62],"identify":[41],"discriminative":[42,112,126],"regions":[43,113],"erase":[45],"them":[46],"increase":[48],"the":[49,56,101,111,155,173,193,196],"variant":[50],"features.":[52,185],"Meanwhile,":[53],"input":[55],"erased":[57,118],"feature":[58,108,148,156],"maps":[59],"another":[61],"discover":[64],"new":[65],"body":[66,97],"regions,":[67],"which":[68,147,169],"effectively":[69],"resist":[70],"occlusion":[71],"key":[73],"parts.":[74],"easily":[76],"perform":[77],"end-to-end":[78],"training":[79],"for":[80,128],"AELM,":[81,129],"novel":[85],"Identity":[86],"model":[87],"based":[88],"on":[89,146,188],"Fully":[90],"Convolutional":[91],"Networks":[92],"(IFCN)":[93],"directly":[95],"obtain":[96],"response":[98],"heatmap":[99],"during":[100],"forward":[102],"pass":[103],"by":[104],"selecting":[105],"corresponding":[106],"class-specific":[107],"map.":[109],"Thus,":[110],"can":[114],"be":[115],"identified":[116],"in":[119,154],"convenient":[121],"way.":[122],"Moreover,":[123],"capture":[125],"region":[127],"present":[131],"Complementary":[133],"Attention":[134],"(CoAM)":[136],"combined":[137],"with":[138],"channel":[139],"spatial":[141],"attention":[142],"automatically":[144],"focus":[145],"types":[149],"positions":[151],"are":[152,164],"meaningful":[153],"maps.":[157],"In":[158],"paper,":[160],"CoAM":[161],"AELM":[163],"cascaded":[165],"into":[166],"one":[167],"module":[168],"applied":[171],"outputs":[174],"different":[176],"convolutional":[177],"layers":[178],"integrate":[180],"mid-":[181],"high-level":[183],"semantic":[184],"Experimental":[186],"results":[187],"three":[189],"challenging":[190],"benchmarks":[191],"demonstrate":[192],"effectiveness":[194],"proposed":[197],"method.":[198]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}