{"id":"https://openalex.org/W4293519352","doi":"https://doi.org/10.1109/icme52920.2022.9859920","title":"Sample-Level and Class-Level Adaptive Training for Face Recognition","display_name":"Sample-Level and Class-Level Adaptive Training for Face Recognition","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4293519352","doi":"https://doi.org/10.1109/icme52920.2022.9859920"},"language":"en","primary_location":{"id":"doi:10.1109/icme52920.2022.9859920","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859920","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","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/A5101518248","display_name":"Mengjiao Wang","orcid":"https://orcid.org/0000-0002-4873-5677"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengjiao Wang","raw_affiliation_strings":["Fujitsu R&D Center Co., Ltd.,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu R&D Center Co., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210159607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044324493","display_name":"Rujie Liu","orcid":"https://orcid.org/0000-0002-4992-7312"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rujie Liu","raw_affiliation_strings":["Fujitsu R&D Center Co., Ltd.,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu R&D Center Co., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210159607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083148100","display_name":"Narishige Abe","orcid":null},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Narishige Abe","raw_affiliation_strings":["Fujitsu Laboratories Ltd.,Kawasaki,Japan","Fujitsu Laboratories Ltd., Kawasaki, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Laboratories Ltd.,Kawasaki,Japan","institution_ids":["https://openalex.org/I2252096349"]},{"raw_affiliation_string":"Fujitsu Laboratories Ltd., Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078617864","display_name":"Tomoaki Matsunami","orcid":null},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Matsunami Tomoaki","raw_affiliation_strings":["Fujitsu Laboratories Ltd.,Kawasaki,Japan","Fujitsu Laboratories Ltd., Kawasaki, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Laboratories Ltd.,Kawasaki,Japan","institution_ids":["https://openalex.org/I2252096349"]},{"raw_affiliation_string":"Fujitsu Laboratories Ltd., Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110616844","display_name":"Hidetsugu Uchida","orcid":null},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Uchida Hidetsugu","raw_affiliation_strings":["Fujitsu Laboratories Ltd.,Kawasaki,Japan","Fujitsu Laboratories Ltd., Kawasaki, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Laboratories Ltd.,Kawasaki,Japan","institution_ids":["https://openalex.org/I2252096349"]},{"raw_affiliation_string":"Fujitsu Laboratories Ltd., Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051600546","display_name":"Septiana Lina","orcid":null},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Septiana Lina","raw_affiliation_strings":["Fujitsu Laboratories Ltd.,Kawasaki,Japan","Fujitsu Laboratories Ltd., Kawasaki, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Laboratories Ltd.,Kawasaki,Japan","institution_ids":["https://openalex.org/I2252096349"]},{"raw_affiliation_string":"Fujitsu Laboratories Ltd., Kawasaki, Japan","institution_ids":["https://openalex.org/I2252096349"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","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/T11448","display_name":"Face recognition and analysis","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/T10057","display_name":"Face and Expression Recognition","score":0.9950000047683716,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9898999929428101,"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/facial-recognition-system","display_name":"Facial recognition system","score":0.671829342842102},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.667034387588501},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6071414947509766},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.550406277179718},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5398551225662231},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5250403881072998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5238035917282104},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44234949350357056},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3506157398223877},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3239513039588928}],"concepts":[{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.671829342842102},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.667034387588501},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6071414947509766},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.550406277179718},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5398551225662231},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5250403881072998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5238035917282104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44234949350357056},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3506157398223877},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3239513039588928},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme52920.2022.9859920","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859920","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2341528187","https://openalex.org/W2404498690","https://openalex.org/W2663800299","https://openalex.org/W2736633948","https://openalex.org/W2871667416","https://openalex.org/W2949007385","https://openalex.org/W2963814162","https://openalex.org/W2969985801","https://openalex.org/W2998469040","https://openalex.org/W3035693354","https://openalex.org/W3101998545","https://openalex.org/W3114705357","https://openalex.org/W3158395175","https://openalex.org/W3169129566"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W4366179611","https://openalex.org/W2384651879"],"abstract_inverted_index":{"Marginal":[0],"softmax":[1],"loss":[2],"function":[3],"has":[4],"been":[5],"widely":[6],"used":[7],"for":[8,42,65,100],"face":[9],"recognition,":[10],"where":[11],"a":[12],"universal":[13],"angular":[14],"margin":[15,41,88,97],"is":[16,98],"added":[17],"between":[18,27],"weight":[19],"prototypes.":[20],"However,":[21],"this":[22,72],"method":[23],"neglects":[24],"the":[25,33,43,56,66,86,91,96,101,107,121],"differences":[26],"classes":[28,47],"and":[29,45,111,131],"samples.":[30,126],"On":[31,55],"class-level,":[32],"imbalanced":[34],"real":[35],"world":[36],"training":[37,122],"dataset":[38],"requires":[39],"different":[40],"head":[44],"tail":[46,102],"to":[48,61,90,115,119],"equally":[49],"squeeze":[50],"each":[51],"class's":[52],"feature":[53],"space.":[54],"sample-level,":[57],"it's":[58],"also":[59],"necessary":[60],"assign":[62,85],"larger":[63],"importance":[64],"hard":[67],"samples":[68,110,113],"during":[69],"training.":[70],"In":[71],"paper,":[73],"we":[74],"address":[75],"these":[76,125],"two":[77,81],"issues":[78],"by":[79],"combining":[80],"strategies:":[82],"(1)":[83],"explicitly":[84],"adaptive":[87,117],"according":[89],"image":[92],"quantity":[93],"so":[94],"that":[95],"enlarged":[99],"classes;":[103],"(2)":[104],"semantically":[105],"identify":[106],"\u2018hard":[108],"positive,":[109],"misclassified":[112],"[1]":[114],"attach":[116],"weights":[118],"increase":[120],"emphasis":[123],"on":[124,129],"Extensive":[127],"experiments":[128],"LFW/CFP/AGEDB":[130],"IJB-B/IJB-C":[132],"show":[133],"our":[134],"method's":[135],"effectiveness.":[136]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}