{"id":"https://openalex.org/W2960563832","doi":"https://doi.org/10.1109/fg.2019.8756619","title":"Valence and Arousal Estimation In-The-Wild with Tensor Methods","display_name":"Valence and Arousal Estimation In-The-Wild with Tensor Methods","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2960563832","doi":"https://doi.org/10.1109/fg.2019.8756619","mag":"2960563832"},"language":"en","primary_location":{"id":"doi:10.1109/fg.2019.8756619","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fg.2019.8756619","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)","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/A5064068793","display_name":"Anna Mitenkova","orcid":null},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Anna Mitenkova","raw_affiliation_strings":["Imperial College, London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imperial College, London, UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006048318","display_name":"Jean Kossaifi","orcid":"https://orcid.org/0000-0002-4445-3429"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jean Kossaifi","raw_affiliation_strings":["Imperial College, London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imperial College, London, UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050734738","display_name":"Yannis Panagakis","orcid":"https://orcid.org/0000-0003-0153-5210"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yannis Panagakis","raw_affiliation_strings":["Imperial College, London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imperial College, London, UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016033078","display_name":"Maja Panti\u0107","orcid":"https://orcid.org/0000-0002-3620-5986"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Maja Pantic","raw_affiliation_strings":["Imperial College, London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imperial College, London, UK","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":null,"apc_paid":null,"fwci":3.3052,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.93493151,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"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/T12303","display_name":"Tensor decomposition and applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9865000247955322,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.982699990272522,"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/valence","display_name":"Valence (chemistry)","score":0.7476114630699158},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6991733908653259},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6687270998954773},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6334800124168396},{"id":"https://openalex.org/keywords/arousal","display_name":"Arousal","score":0.5976399779319763},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5687337517738342},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47519171237945557},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.44275179505348206},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23167026042938232},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2038593292236328},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.17983630299568176},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.09790840744972229}],"concepts":[{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.7476114630699158},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6991733908653259},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6687270998954773},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6334800124168396},{"id":"https://openalex.org/C36951298","wikidata":"https://www.wikidata.org/wiki/Q379784","display_name":"Arousal","level":2,"score":0.5976399779319763},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5687337517738342},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47519171237945557},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.44275179505348206},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23167026042938232},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2038593292236328},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.17983630299568176},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.09790840744972229},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"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/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/fg.2019.8756619","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fg.2019.8756619","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W1970176196","https://openalex.org/W1981918162","https://openalex.org/W2010692358","https://openalex.org/W2016077242","https://openalex.org/W2024165284","https://openalex.org/W2037441721","https://openalex.org/W2060645041","https://openalex.org/W2062614785","https://openalex.org/W2108598243","https://openalex.org/W2110885456","https://openalex.org/W2121606425","https://openalex.org/W2136002544","https://openalex.org/W2158509166","https://openalex.org/W2161782031","https://openalex.org/W2544822491","https://openalex.org/W2549362047","https://openalex.org/W2587982884","https://openalex.org/W2621097878","https://openalex.org/W2737226596","https://openalex.org/W2738900641","https://openalex.org/W2779220966","https://openalex.org/W2899771611","https://openalex.org/W2910165986","https://openalex.org/W2949117887","https://openalex.org/W2951251599","https://openalex.org/W2952198983","https://openalex.org/W2963713173","https://openalex.org/W3081927587","https://openalex.org/W4249348869","https://openalex.org/W6638667902","https://openalex.org/W6720207886","https://openalex.org/W6729203000","https://openalex.org/W6729745744","https://openalex.org/W6741969688","https://openalex.org/W6747613451","https://openalex.org/W6756040250","https://openalex.org/W6758275190"],"related_works":["https://openalex.org/W2029072726","https://openalex.org/W91913183","https://openalex.org/W2936882366","https://openalex.org/W2736893848","https://openalex.org/W2128698257","https://openalex.org/W1544055438","https://openalex.org/W3003450285","https://openalex.org/W2013608943","https://openalex.org/W4399628019","https://openalex.org/W2085024878"],"abstract_inverted_index":{"While":[0],"it":[1,15],"is":[2,16],"relatively":[3],"easy":[4],"and":[5,11,75,192,203,219],"natural":[6],"for":[7,20,194],"humans":[8],"to":[9,80,92,118,130,138],"detect":[10],"interpret":[12],"non-verbal":[13],"cues,":[14],"a":[17,59,83,157,166,173],"hard":[18],"task":[19],"computer":[21],"systems.":[22],"Automatic":[23],"recognition":[24],"of":[25,31,73,86,94,144,165,201],"emotions":[26],"has":[27],"been":[28,45],"the":[29,35,40,47,95,120,123,132,136,141,147,151,163,214,217],"subject":[30],"extensive":[32],"studies":[33],"in":[34,69,107,109,122,146,162,199,216],"past":[36],"decade,":[37],"but":[38],"despite":[39],"various":[41],"methods":[42,211],"that":[43,98,209],"have":[44,135],"implemented,":[46],"problem":[48],"remains":[49],"challenging.":[50],"In":[51,128],"particular,":[52],"most":[53],"existing":[54],"works":[55],"focus":[56],"on":[57,100,186,220],"predicting":[58,70],"set":[60],"discrete":[61],"stereotypical":[62],"emotion":[63],"categories.":[64],"We":[65,154,170,182],"are":[66,78,105],"instead":[67],"interested":[68,106],"continuous":[71],"values":[72],"valence":[74,202],"arousal,":[76],"which":[77],"able":[79],"model":[81,158],"accurately":[82],"broader":[84],"range":[85],"spontaneous":[87],"emotions.":[88],"Moreover,":[89],"as":[90],"opposed":[91],"much":[93],"prior":[96],"work":[97],"focused":[99],"controlled":[101],"(laboratory)":[102],"conditions,":[103],"we":[104,116],"analysis":[108],"naturalist":[110],"(in-the-wild)":[111],"conditions.":[112],"To":[113],"do":[114],"so,":[115],"propose":[117],"leverage":[119,213],"structure":[121,215],"data":[124,218],"using":[125],"tensor":[126],"methods.":[127,224],"addition":[129],"preserving":[131],"structure,":[133],"these":[134],"potential":[137],"also":[139],"reduce":[140],"total":[142],"number":[143],"parameters":[145],"models,":[148],"thus":[149],"improving":[150],"computational":[152],"performance.":[153],"first":[155],"consider":[156],"with":[159],"analytic":[160],"solution":[161],"form":[164],"Tucker":[167],"Tensor":[168,179],"Regression.":[169],"then":[171],"investigate":[172],"deep,":[174],"gradient":[175],"based":[176],"method,":[177],"namely":[178],"Regression":[180],"Networks.":[181],"perform":[183],"thorough":[184],"experiments":[185],"two":[187],"publicly":[188],"available":[189],"databases,":[190],"AFEW-VA":[191],"SEWA,":[193],"facial":[195],"affect":[196],"estimation":[197],"in-the-wild,":[198],"terms":[200],"arousal":[204],"levels.":[205],"Experimental":[206],"results":[207],"demonstrate":[208],"tensor-based":[210],"successfully":[212],"average":[221],"outperform":[222],"baseline":[223]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}