{"id":"https://openalex.org/W4205885905","doi":"https://doi.org/10.1109/tmm.2022.3141604","title":"Deep Margin-Sensitive Representation Learning for Cross-Domain Facial Expression Recognition","display_name":"Deep Margin-Sensitive Representation Learning for Cross-Domain Facial Expression Recognition","publication_year":2022,"publication_date":"2022-01-10","ids":{"openalex":"https://openalex.org/W4205885905","doi":"https://doi.org/10.1109/tmm.2022.3141604"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2022.3141604","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2022.3141604","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"},"type":"article","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/A5009278704","display_name":"Yingjian Li","orcid":"https://orcid.org/0000-0002-0653-4535"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yingjian Li","raw_affiliation_strings":["School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100459168","display_name":"Zheng Zhang","orcid":"https://orcid.org/0000-0003-1470-6998"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Zhang","raw_affiliation_strings":["School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055183002","display_name":"Bingzhi Chen","orcid":"https://orcid.org/0000-0002-2497-6214"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingzhi Chen","raw_affiliation_strings":["School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030843117","display_name":"Guangming Lu","orcid":"https://orcid.org/0000-0003-1578-2634"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangming Lu","raw_affiliation_strings":["School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100325058","display_name":"David Zhang","orcid":"https://orcid.org/0000-0002-5027-5286"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]},{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"David Zhang","raw_affiliation_strings":["School of Data Science, The Chinese University of Hong Kong, Shenzhen, China","Shenzhen Research Institute of Big Data, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Data Science, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]},{"raw_affiliation_string":"Shenzhen Research Institute of Big Data, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China","institution_ids":["https://openalex.org/I4210099586"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5009278704"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":10.3174,"has_fulltext":false,"cited_by_count":56,"citation_normalized_percentile":{"value":0.9869945,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"25","issue":null,"first_page":"1359","last_page":"1373"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.989799976348877,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9894999861717224,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.9330974221229553},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8166013956069946},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6824501156806946},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6477999687194824},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6402477622032166},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5985110998153687},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5101307034492493},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4833659529685974},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.47650519013404846},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.46746304631233215},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4581974148750305},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45587486028671265},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.437843918800354},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.11182469129562378}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9330974221229553},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8166013956069946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6824501156806946},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6477999687194824},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6402477622032166},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5985110998153687},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5101307034492493},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4833659529685974},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.47650519013404846},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.46746304631233215},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4581974148750305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45587486028671265},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.437843918800354},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.11182469129562378},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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},{"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2022.3141604","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2022.3141604","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G332974726","display_name":null,"funder_award_id":"GXWD20201230155427003-20200824103320001","funder_id":"https://openalex.org/F4320335803","funder_display_name":"Shenzhen Fundamental Research and Discipline Layout project"},{"id":"https://openalex.org/G5694134097","display_name":null,"funder_award_id":"JCYJ20210324132210025","funder_id":"https://openalex.org/F4320335803","funder_display_name":"Shenzhen Fundamental Research and Discipline Layout project"},{"id":"https://openalex.org/G5726720604","display_name":null,"funder_award_id":"62176077","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G682760380","display_name":null,"funder_award_id":"2019B1515120055","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G7115490737","display_name":null,"funder_award_id":"62002085","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335803","display_name":"Shenzhen Fundamental Research and Discipline Layout project","ror":null},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W2014185685","https://openalex.org/W2032484416","https://openalex.org/W2035372623","https://openalex.org/W2041616772","https://openalex.org/W2051526232","https://openalex.org/W2065379720","https://openalex.org/W2103943262","https://openalex.org/W2104094955","https://openalex.org/W2106115875","https://openalex.org/W2106947945","https://openalex.org/W2117645142","https://openalex.org/W2137306662","https://openalex.org/W2139916508","https://openalex.org/W2161634108","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2244142460","https://openalex.org/W2745497104","https://openalex.org/W2755860313","https://openalex.org/W2767405849","https://openalex.org/W2799041689","https://openalex.org/W2805781993","https://openalex.org/W2889978276","https://openalex.org/W2890077417","https://openalex.org/W2901505625","https://openalex.org/W2902346685","https://openalex.org/W2904483377","https://openalex.org/W2908186737","https://openalex.org/W2940983946","https://openalex.org/W2968584961","https://openalex.org/W2980096013","https://openalex.org/W2982204955","https://openalex.org/W2990138404","https://openalex.org/W2998061714","https://openalex.org/W3003720578","https://openalex.org/W3026860374","https://openalex.org/W3035336958","https://openalex.org/W3048580993","https://openalex.org/W3092802919","https://openalex.org/W3104842364","https://openalex.org/W3106153666","https://openalex.org/W3112113890","https://openalex.org/W3122081138","https://openalex.org/W3124054989","https://openalex.org/W3154541964","https://openalex.org/W3158888011","https://openalex.org/W3187532107","https://openalex.org/W3189369550","https://openalex.org/W3205640762","https://openalex.org/W6637373629","https://openalex.org/W6639480849","https://openalex.org/W6683633756","https://openalex.org/W6713955831","https://openalex.org/W6734209382","https://openalex.org/W6747837047","https://openalex.org/W6750109254","https://openalex.org/W6751635369","https://openalex.org/W6754278344","https://openalex.org/W6757410267","https://openalex.org/W6763066025"],"related_works":["https://openalex.org/W2740361506","https://openalex.org/W3105973526","https://openalex.org/W3095487414","https://openalex.org/W3208297503","https://openalex.org/W3119773509","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4288048773","https://openalex.org/W2901026139","https://openalex.org/W4388405611"],"abstract_inverted_index":{"Cross-domain":[0],"Facial":[1],"Expression":[2],"Recognition":[3],"(FER)":[4],"aims":[5],"to":[6,16,24,51,177,187,207],"safely":[7],"transfer":[8,64,211],"the":[9,25,32,43,101,128,142,147,167,180,189,209],"learned":[10],"knowledge":[11,124],"from":[12],"labeled":[13],"source":[14,105,213],"data":[15,106],"unlabeled":[17],"target":[18,112,160],"data,":[19,113],"which":[20,59,81,114,150,183],"is":[21],"challenging":[22],"due":[23],"subtle":[26],"difference":[27],"between":[28,35],"various":[29],"expressions":[30],"and":[31,107,121,145],"large":[33],"discrepancy":[34],"domains.":[36],"Existing":[37],"methods":[38],"mainly":[39],"focus":[40],"on":[41,100,218],"reducing":[42],"domain":[44,89],"shift":[45],"for":[46,55],"transferable":[47,122,153],"features":[48,86],"but":[49],"fail":[50],"learn":[52],"discriminative":[53,85,117,152],"representations":[54],"recognizing":[56],"facial":[57],"expression,":[58],"may":[60],"result":[61],"in":[62,200,203],"negative":[63],"under":[65],"cross-domain":[66],"settings.":[67],"To":[68],"this":[69],"end,":[70],"we":[71,92,132,170],"propose":[72],"a":[73,94,134,172],"novel":[74],"Deep":[75],"Margin-Sensitive":[76],"Representation":[77],"Learning":[78],"(DMSRL)":[79],"framework,":[80],"can":[82,115,225],"extract":[83],"multi-level":[84,173],"during":[87],"sematic-aware":[88],"adaptation.":[90],"Specifically,":[91],"design":[93],"semantic":[95],"metric":[96],"learning":[97,120,155],"module":[98,138,176],"based":[99],"category":[102,129],"prior":[103],"of":[104,111,164,212],"generated":[108],"pseudo":[109,159],"labels":[110],"facilitate":[116],"intra-domain":[118],"representation":[119],"inter-domain":[123],"discovery":[125],"by":[126,139,156],"enlarging":[127],"margin.":[130],"Moreover,":[131],"develop":[133],"mutual":[135],"information":[136,186],"minimization":[137],"simultaneously":[140],"distilling":[141],"domain-invariant":[143],"components":[144],"eliminating":[146],"domain-sensitive":[148],"ones,":[149,182],"benefits":[151],"feature":[154,174],"generating":[157],"accurate":[158],"labels.":[161],"Furthermore,":[162],"instead":[163],"only":[165],"utilizing":[166],"global":[168],"features,":[169],"formulate":[171],"extracting":[175],"concurrently":[178],"get":[179],"local":[181],"contain":[184],"detailed":[185],"distinguish":[188],"small":[190],"changes":[191],"among":[192],"different":[193],"expressions.":[194],"These":[195],"modules":[196],"are":[197],"jointly":[198],"utilized":[199],"our":[201,223],"DMSRL":[202,224],"an":[204],"end-to-end":[205],"manner":[206],"ensure":[208],"positive":[210],"knowledge.":[214],"Extensive":[215],"experimental":[216],"results":[217],"seven":[219],"databases":[220],"demonstrate":[221],"that":[222],"achieve":[226],"superior":[227],"performance":[228],"against":[229],"state-of-the-art":[230],"baselines.":[231]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":7}],"updated_date":"2026-03-13T16:22:10.518609","created_date":"2025-10-10T00:00:00"}