{"id":"https://openalex.org/W4312797523","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892729","title":"Multi-scale Local Region Relation Attention in Convolutional Neural Networks for Facial Action Unit Intensity Prediction","display_name":"Multi-scale Local Region Relation Attention in Convolutional Neural Networks for Facial Action Unit Intensity Prediction","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312797523","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892729"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892729","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892729","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-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/A5023784698","display_name":"Anrui Wang","orcid":"https://orcid.org/0000-0003-3710-2345"},"institutions":[{"id":"https://openalex.org/I4210142748","display_name":"Shandong Academy of Sciences","ror":"https://ror.org/04y8d6y55","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Anrui Wang","raw_affiliation_strings":["School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences),Jinan,China","School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences),Jinan,China","institution_ids":["https://openalex.org/I152269853","https://openalex.org/I4210142748"]},{"raw_affiliation_string":"School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China","institution_ids":["https://openalex.org/I152269853","https://openalex.org/I4210142748"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014011487","display_name":"Weiyang Chen","orcid":"https://orcid.org/0000-0001-8430-7381"},"institutions":[{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]},{"id":"https://openalex.org/I4210142748","display_name":"Shandong Academy of Sciences","ror":"https://ror.org/04y8d6y55","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiyang Chen","raw_affiliation_strings":["School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences),Jinan,China","School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences),Jinan,China","institution_ids":["https://openalex.org/I152269853","https://openalex.org/I4210142748"]},{"raw_affiliation_string":"School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China","institution_ids":["https://openalex.org/I152269853","https://openalex.org/I4210142748"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5023784698"],"corresponding_institution_ids":["https://openalex.org/I152269853","https://openalex.org/I4210142748"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15556823,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9984999895095825,"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":0.9984999895095825,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9951000213623047,"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/T11094","display_name":"Face Recognition and Perception","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6750365495681763},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6716564893722534},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6031579971313477},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5625722408294678},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.543576717376709},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5258094668388367},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47860902547836304},{"id":"https://openalex.org/keywords/intensity","display_name":"Intensity (physics)","score":0.47767549753189087},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.41798821091651917},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.328591525554657},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2210012674331665},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.0748986303806305}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6750365495681763},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6716564893722534},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6031579971313477},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5625722408294678},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.543576717376709},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5258094668388367},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47860902547836304},{"id":"https://openalex.org/C93038891","wikidata":"https://www.wikidata.org/wiki/Q1061524","display_name":"Intensity (physics)","level":2,"score":0.47767549753189087},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.41798821091651917},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.328591525554657},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2210012674331665},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0748986303806305},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892729","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892729","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4890061939","display_name":null,"funder_award_id":"12026210,12026209","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1588539311","https://openalex.org/W1622676895","https://openalex.org/W1665214252","https://openalex.org/W1836465849","https://openalex.org/W1964277727","https://openalex.org/W1980331490","https://openalex.org/W2037891270","https://openalex.org/W2045472600","https://openalex.org/W2069441227","https://openalex.org/W2115252128","https://openalex.org/W2136655611","https://openalex.org/W2141403362","https://openalex.org/W2604672468","https://openalex.org/W2612215259","https://openalex.org/W2743060918","https://openalex.org/W2756460808","https://openalex.org/W2784208377","https://openalex.org/W2789992948","https://openalex.org/W2798833386","https://openalex.org/W2799151537","https://openalex.org/W2884585870","https://openalex.org/W2884989807","https://openalex.org/W2898113613","https://openalex.org/W2963713173","https://openalex.org/W2964322530","https://openalex.org/W2995846001","https://openalex.org/W2996882407","https://openalex.org/W3035542935","https://openalex.org/W3101614002","https://openalex.org/W3102025332","https://openalex.org/W3107794057","https://openalex.org/W3107810273","https://openalex.org/W3137313106","https://openalex.org/W3175851380","https://openalex.org/W3178540223","https://openalex.org/W4287906843","https://openalex.org/W4295312788","https://openalex.org/W6635087628","https://openalex.org/W6637242042","https://openalex.org/W6638667902","https://openalex.org/W6677618333","https://openalex.org/W6679997575","https://openalex.org/W6735920323","https://openalex.org/W6753412334","https://openalex.org/W6766978945","https://openalex.org/W6771335037","https://openalex.org/W6785548076","https://openalex.org/W6786372484","https://openalex.org/W6797311819","https://openalex.org/W6803376173","https://openalex.org/W6840709385"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175"],"abstract_inverted_index":{"Facial":[0],"Action":[1],"Unit":[2],"(FAU)":[3],"intensity":[4,40,53,79,116,185,223],"can":[5,22],"describe":[6],"the":[7,12,19,26,34,58,89,122,128,144,154,192,212],"degree":[8],"of":[9,14,28,70,131,160,214],"change":[10],"in":[11,37],"appearance":[13],"a":[15,102,215],"specific":[16],"location":[17],"on":[18,109,191],"face":[20,132],"and":[21,84,173,197,210],"be":[23],"used":[24],"for":[25,51,114,221],"analysis":[27],"human":[29],"facial":[30,170],"behavior.":[31],"Due":[32],"to":[33,66,134,152,181,207],"subtle":[35],"changes":[36],"FAU,":[38],"FAU":[39,52,59,78,115,184,222],"prediction":[41,54],"still":[42],"faces":[43],"great":[44],"challenges.":[45],"Previous":[46],"works":[47],"using":[48,178],"attention":[49,64,106,150,156,166,219],"mechanisms":[50,65],"either":[55],"simply":[56],"crop":[57],"regions":[60],"or":[61],"directly":[62],"use":[63,143],"obtain":[67],"local":[68,136,147,155,165],"representations":[69],"FAUs,":[71],"but":[72],"these":[73],"methods":[74,209],"do":[75],"not":[76],"capture":[77,135],"features":[80,137,159,176],"at":[81],"different":[82],"scales":[83],"locations":[85],"well.":[86],"In":[87,97],"addition,":[88],"dependencies":[90],"between":[91,124],"FAUs":[92,125],"also":[93],"contain":[94],"important":[95],"information.":[96],"this":[98],"paper,":[99],"we":[100,119,142,163],"propose":[101],"multi-scale":[103,146,216],"local-region":[104,217],"relational":[105,149,158,218],"model":[107,151,220],"based":[108],"convolutional":[110],"neural":[111],"networks":[112],"(CNN)":[113],"prediction.":[117,186,224],"Specifically,":[118],"first":[120],"reflect":[121],"relationship":[123,168],"by":[126],"adjusting":[127],"luminance":[129],"values":[130],"images":[133],"with":[138],"pixel-level":[139],"relationships.":[140],"Then,":[141],"introduced":[145],"area":[148],"extract":[153],"latent":[157],"FAU.":[161],"Finally,":[162],"combine":[164],"potential":[167],"features,":[169],"geometry":[171],"information,":[172],"deep":[174],"global":[175],"captured":[177],"an":[179],"autoencoder":[180],"achieve":[182],"robust":[183],"The":[187],"method":[188,203],"is":[189],"evaluated":[190],"public":[193],"benchmark":[194],"dataset":[195],"DISFA,":[196],"experimental":[198],"results":[199],"show":[200],"that":[201],"our":[202],"achieves":[204],"comparable":[205],"performance":[206],"state-of-the-art":[208],"validates":[211],"effectiveness":[213]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}