{"id":"https://openalex.org/W4282931913","doi":"https://doi.org/10.1109/tcyb.2022.3173356","title":"Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples","display_name":"Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples","publication_year":2022,"publication_date":"2022-05-23","ids":{"openalex":"https://openalex.org/W4282931913","doi":"https://doi.org/10.1109/tcyb.2022.3173356","pmid":"https://pubmed.ncbi.nlm.nih.gov/35604981"},"language":"en","primary_location":{"id":"doi:10.1109/tcyb.2022.3173356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2022.3173356","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Cybernetics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://discovery.ucl.ac.uk/10149136/1/YanYan-YouzeXu-TCYB-2022.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100395059","display_name":"Yan Yan","orcid":"https://orcid.org/0000-0002-3674-7160"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Yan","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0002-3674-7160","affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113952700","display_name":"Youze Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youze Xu","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079361172","display_name":"Jing\u2010Hao Xue","orcid":"https://orcid.org/0000-0003-1174-610X"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jing-Hao Xue","raw_affiliation_strings":["Department of Statistical Science, University College London, London, U.K"],"raw_orcid":"https://orcid.org/0000-0003-1174-610X","affiliations":[{"raw_affiliation_string":"Department of Statistical Science, University College London, London, U.K","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015745132","display_name":"Yang Lu","orcid":"https://orcid.org/0000-0002-3497-9611"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Lu","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0002-3497-9611","affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044594971","display_name":"Hanzi Wang","orcid":"https://orcid.org/0000-0002-6913-9786"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanzi Wang","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0002-6913-9786","affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031854562","display_name":"Wentao Zhu","orcid":"https://orcid.org/0000-0001-9290-1778"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Zhu","raw_affiliation_strings":["Zhejiang Lab, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9290-1778","affiliations":[{"raw_affiliation_string":"Zhejiang Lab, Hangzhou, China","institution_ids":["https://openalex.org/I4210123185"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6479,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.65058528,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"53","issue":"11","first_page":"7071","last_page":"7084"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.9980999827384949,"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":0.7039246559143066},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5967420935630798},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5191844701766968},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4659709930419922},{"id":"https://openalex.org/keywords/drop","display_name":"Drop (telecommunication)","score":0.4623817801475525},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.4147948920726776},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.35228753089904785}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7039246559143066},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5967420935630798},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5191844701766968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4659709930419922},{"id":"https://openalex.org/C2781345722","wikidata":"https://www.wikidata.org/wiki/Q5308388","display_name":"Drop (telecommunication)","level":2,"score":0.4623817801475525},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.4147948920726776},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35228753089904785},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tcyb.2022.3173356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2022.3173356","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Cybernetics","raw_type":"journal-article"},{"id":"pmid:35604981","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35604981","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on cybernetics","raw_type":null},{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:10149136","is_oa":true,"landing_page_url":"https://discovery.ucl.ac.uk/id/eprint/10149136/","pdf_url":"https://discovery.ucl.ac.uk/10149136/1/YanYan-YouzeXu-TCYB-2022.pdf","source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Cybernetics (2022) (In press).","raw_type":"Article"}],"best_oa_location":{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:10149136","is_oa":true,"landing_page_url":"https://discovery.ucl.ac.uk/id/eprint/10149136/","pdf_url":"https://discovery.ucl.ac.uk/10149136/1/YanYan-YouzeXu-TCYB-2022.pdf","source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Cybernetics (2022) (In press).","raw_type":"Article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2022121445","display_name":null,"funder_award_id":"U21A20514","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G375371377","display_name":null,"funder_award_id":"3502Z20206046","funder_id":"https://openalex.org/F4320319601","funder_display_name":"Youth Innovation Foundation of Xiamen"},{"id":"https://openalex.org/G5037251186","display_name":null,"funder_award_id":"62002302","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6528361795","display_name":"\u590d\u6742\u573a\u666f\u4e0b\u7684\u957f\u7a0b\u76ee\u6807\u8ddf\u8e2a\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61872307","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8055606312","display_name":"\u57fa\u4e8e\u9c81\u68d2\u6a21\u578b\u62df\u5408\u548c\u6df1\u5ea6\u5b66\u4e60\u7684\u4eba\u8138\u5c5e\u6027\u8bc6\u522b\u65b9\u6cd5\u7814\u7a76","funder_award_id":"62071404","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320319601","display_name":"Youth Innovation Foundation of Xiamen","ror":null},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4282931913.pdf","grobid_xml":"https://content.openalex.org/works/W4282931913.grobid-xml"},"referenced_works_count":81,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1599238028","https://openalex.org/W1605688901","https://openalex.org/W1834627138","https://openalex.org/W1907729166","https://openalex.org/W1921293667","https://openalex.org/W2056935845","https://openalex.org/W2096733369","https://openalex.org/W2108598243","https://openalex.org/W2118978333","https://openalex.org/W2126448884","https://openalex.org/W2128560777","https://openalex.org/W2136903812","https://openalex.org/W2140641199","https://openalex.org/W2147414309","https://openalex.org/W2167460663","https://openalex.org/W2194775991","https://openalex.org/W2204750386","https://openalex.org/W2296073425","https://openalex.org/W2311038409","https://openalex.org/W2440599146","https://openalex.org/W2585635281","https://openalex.org/W2604463754","https://openalex.org/W2605124728","https://openalex.org/W2622263826","https://openalex.org/W2738069262","https://openalex.org/W2752971446","https://openalex.org/W2766111649","https://openalex.org/W2767106145","https://openalex.org/W2771076441","https://openalex.org/W2782522780","https://openalex.org/W2784560833","https://openalex.org/W2798869704","https://openalex.org/W2800167607","https://openalex.org/W2803187616","https://openalex.org/W2804665167","https://openalex.org/W2832876791","https://openalex.org/W2887878805","https://openalex.org/W2896249043","https://openalex.org/W2896277673","https://openalex.org/W2899771611","https://openalex.org/W2918288062","https://openalex.org/W2936503027","https://openalex.org/W2948333011","https://openalex.org/W2954148997","https://openalex.org/W2962786991","https://openalex.org/W2963113370","https://openalex.org/W2963212406","https://openalex.org/W2963351448","https://openalex.org/W2963596856","https://openalex.org/W2963735582","https://openalex.org/W2963839617","https://openalex.org/W2963936326","https://openalex.org/W2964050365","https://openalex.org/W2971609010","https://openalex.org/W2982247743","https://openalex.org/W2988966271","https://openalex.org/W3012846653","https://openalex.org/W3035336958","https://openalex.org/W3103850820","https://openalex.org/W3138358913","https://openalex.org/W3159359277","https://openalex.org/W3182380222","https://openalex.org/W3182879855","https://openalex.org/W3200445214","https://openalex.org/W4210666190","https://openalex.org/W4226488683","https://openalex.org/W4239510810","https://openalex.org/W6631190155","https://openalex.org/W6640298173","https://openalex.org/W6680202767","https://openalex.org/W6680832002","https://openalex.org/W6739622702","https://openalex.org/W6743885473","https://openalex.org/W6751420435","https://openalex.org/W6751647823","https://openalex.org/W6755364507","https://openalex.org/W6756040250","https://openalex.org/W6767249749","https://openalex.org/W6793831464","https://openalex.org/W6794813173"],"related_works":["https://openalex.org/W2599472179","https://openalex.org/W4323057981","https://openalex.org/W4375867731","https://openalex.org/W3178607569","https://openalex.org/W4308408209","https://openalex.org/W4301783946","https://openalex.org/W3196952692","https://openalex.org/W3177025895","https://openalex.org/W4380075502","https://openalex.org/W4285161415"],"abstract_inverted_index":{"Person":[0],"attribute":[1,156,160],"recognition":[2,157],"(PAR)":[3],"aims":[4],"to":[5,48,94],"simultaneously":[6],"predict":[7],"multiple":[8],"attributes":[9,31,77],"of":[10,38,56,117,128,174],"a":[11,62,101,110,134],"person.":[12],"Existing":[13],"deep":[14],"learning-based":[15],"PAR":[16,43,152,184],"methods":[17,24],"have":[18,32],"achieved":[19],"impressive":[20],"performance.":[21,50],"Unfortunately,":[22],"these":[23],"usually":[25],"ignore":[26],"the":[27,36,42,53,73,76,87,106,114,126,129,140,164],"fact":[28],"that":[29,163],"different":[30],"an":[33,82],"imbalance":[34],"in":[35,41,81,172],"number":[37],"noisy-labeled":[39,58,88,119],"samples":[40,120],"training":[44],"datasets,":[45],"thus":[46],"leading":[47],"suboptimal":[49],"To":[51,124],"address":[52],"above":[54],"problem":[55],"imbalanced":[57,118],"samples,":[59],"we":[60,132],"propose":[61],"novel":[63],"and":[64,143,158,178],"effective":[65],"loss":[66,69,142],"called":[67],"drop":[68,74,103,141],"for":[70,105],"PAR.":[71],"In":[72,85],"loss,":[75,131],"are":[78,91,98],"treated":[79],"differently":[80],"easy-to-hard":[83],"way.":[84],"particular,":[86],"candidates,":[89],"which":[90],"identified":[92],"according":[93],"their":[95],"gradient":[96],"norms,":[97],"dropped":[99],"with":[100],"higher":[102],"rate":[104],"harder":[107],"attribute.":[108],"Such":[109],"manner":[111],"adaptively":[112],"alleviates":[113],"adverse":[115],"effect":[116],"on":[121,139,149],"model":[122,137],"learning.":[123],"illustrate":[125],"effectiveness":[127],"proposed":[130,165],"train":[133],"simple":[135],"ResNet-50":[136],"based":[138],"term":[144],"it":[145],"DropNet.":[146],"Experimental":[147],"results":[148],"two":[150],"representative":[151],"tasks":[153],"(including":[154],"facial":[155],"pedestrian":[159],"recognition)":[161],"demonstrate":[162],"DropNet":[166],"achieves":[167],"comparable":[168],"or":[169],"better":[170],"performance":[171],"terms":[173],"both":[175],"balanced":[176],"accuracy":[177,180],"classification":[179],"over":[181],"several":[182],"state-of-the-art":[183],"methods.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}
