{"id":"https://openalex.org/W7160285929","doi":"https://doi.org/10.1109/wacv61042.2026.00825","title":"From Cognitive Priors to Instance Semantics: A Unified Framework for Multi-task Affective Computing","display_name":"From Cognitive Priors to Instance Semantics: A Unified Framework for Multi-task Affective Computing","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160285929","doi":"https://doi.org/10.1109/wacv61042.2026.00825"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00825","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00825","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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/A5135334948","display_name":"Guanyu Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanyu Hu","raw_affiliation_strings":["Xi&#x2019;an Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029879679","display_name":"Dimitrios Kollias","orcid":"https://orcid.org/0000-0002-8188-3751"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Dimitrios Kollias","raw_affiliation_strings":["Queen Mary University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Queen Mary University of London","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135302586","display_name":"Xinyu Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Yang","raw_affiliation_strings":["Xi&#x2019;an Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.51751799,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8551","last_page":"8562"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.18080000579357147,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.18080000579357147,"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/T12607","display_name":"Personal Information Management and User Behavior","score":0.14959999918937683,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.04910000041127205,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5680000185966492},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.49639999866485596},{"id":"https://openalex.org/keywords/cognitive-computing","display_name":"Cognitive computing","score":0.30809998512268066},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.29260000586509705},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.26660001277923584}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5773000121116638},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5680000185966492},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.49639999866485596},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4645000100135803},{"id":"https://openalex.org/C92298750","wikidata":"https://www.wikidata.org/wiki/Q17008161","display_name":"Cognitive computing","level":3,"score":0.30809998512268066},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2806999981403351},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2694000005722046},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.26339998841285706},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C161407221","wikidata":"https://www.wikidata.org/wiki/Q4382939","display_name":"Cognitive model","level":3,"score":0.25870001316070557}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00825","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00825","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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":72,"referenced_works":["https://openalex.org/W1520861770","https://openalex.org/W1588539311","https://openalex.org/W1980331490","https://openalex.org/W2045472600","https://openalex.org/W2343758848","https://openalex.org/W2436394355","https://openalex.org/W2655404332","https://openalex.org/W2713788831","https://openalex.org/W2734289922","https://openalex.org/W2738672149","https://openalex.org/W2745497104","https://openalex.org/W2765291577","https://openalex.org/W2789992948","https://openalex.org/W2798536775","https://openalex.org/W2896277673","https://openalex.org/W2963856015","https://openalex.org/W2980113592","https://openalex.org/W2982152811","https://openalex.org/W3004993066","https://openalex.org/W3007476359","https://openalex.org/W3010857996","https://openalex.org/W3108527469","https://openalex.org/W3126471623","https://openalex.org/W3126750668","https://openalex.org/W3167584510","https://openalex.org/W3175546442","https://openalex.org/W3175851380","https://openalex.org/W3179103990","https://openalex.org/W3209397829","https://openalex.org/W3210643189","https://openalex.org/W4210338274","https://openalex.org/W4214934944","https://openalex.org/W4285250231","https://openalex.org/W4285601347","https://openalex.org/W4292794012","https://openalex.org/W4292829032","https://openalex.org/W4297841562","https://openalex.org/W4312465353","https://openalex.org/W4312907849","https://openalex.org/W4321353834","https://openalex.org/W4321354049","https://openalex.org/W4323057890","https://openalex.org/W4372346643","https://openalex.org/W4385801060","https://openalex.org/W4385805114","https://openalex.org/W4385815442","https://openalex.org/W4385815509","https://openalex.org/W4386066245","https://openalex.org/W4386161521","https://openalex.org/W4386572391","https://openalex.org/W4389917421","https://openalex.org/W4390871994","https://openalex.org/W4390873312","https://openalex.org/W4393159266","https://openalex.org/W4393639583","https://openalex.org/W4400527893","https://openalex.org/W4402915714","https://openalex.org/W4402916143","https://openalex.org/W4402916216","https://openalex.org/W4402916217","https://openalex.org/W4402916401","https://openalex.org/W4402916641","https://openalex.org/W4402917254","https://openalex.org/W4402979749","https://openalex.org/W4403420692","https://openalex.org/W4404239023","https://openalex.org/W4408280641","https://openalex.org/W4410773791","https://openalex.org/W4410774753","https://openalex.org/W4415538093","https://openalex.org/W7131077243","https://openalex.org/W7138182223"],"related_works":[],"abstract_inverted_index":{"Understanding":[0],"human":[1],"affect":[2],"via":[3,149],"Valence-Arousal,":[4],"Expressions,":[5],"and":[6,43,58,69,86,108,140,151,162],"Action":[7],"Unit":[8],"is":[9,165],"essential":[10],"for":[11,50,89],"human-machine":[12],"interaction.":[13],"While":[14],"recent":[15],"multi-task":[16],"learning":[17],"(MTL)":[18],"methods":[19],"seek":[20],"to":[21,102,121,133],"unify":[22],"these":[23],"tasks,":[24,52],"they":[25],"overlook":[26],"three":[27,37],"key":[28],"challenges:":[29],"(i)":[30,124],"the":[31],"absence":[32],"of":[33],"unified":[34],"modeling":[35],"all":[36,51],"affective":[38],"task":[39,60],"types:":[40],"regression,":[41],"detection,":[42],"classification;":[44],"(ii)":[45,141],"reliance":[46],"on":[47],"complete":[48],"annotations":[49,107],"leaving":[53],"disjoint":[54],"single-task":[55],"datasets":[56,158],"underutilized;":[57],"(iii)":[59],"conflicts":[61],"caused":[62],"by":[63],"Noisy":[64],"Gradients,":[65],"Negative":[66],"Transfer":[67],"(NT),":[68],"Task-specific":[70],"Performance":[71],"Misalignment":[72],"(TPM).":[73],"We":[74],"introduce":[75,117],"COIN,":[76],"a":[77,95],"novel":[78],"two-stage":[79],"MTL":[80],"framework":[81],"that":[82],"bridges":[83],"Cognitive":[84],"Priors":[85],"Instance":[87],"Semantics":[88],"robust":[90],"training.":[91],"First,":[92],"we":[93,116],"design":[94],"cognitively":[96],"guided":[97],"cross-task":[98],"label":[99],"induction":[100],"strategy":[101],"propagate":[103],"supervision":[104],"under":[105,137],"sparse":[106],"mitigate":[109],"NT,":[110],"yielding":[111],"strong":[112],"task-specific":[113],"CogXperts.":[114],"Second,":[115],"two":[118],"complementary":[119],"branches":[120],"address":[122],"TPM:":[123],"Task-Specific":[125],"Branch:":[126,144],"transferring":[127],"cognitive":[128],"knowledge":[129],"from":[130],"task-optimal":[131],"CogXperts":[132],"jointly":[134],"optimize":[135],"objectives":[136],"partial":[138],"supervision,":[139],"Semantic":[142],"Alignment":[143],"enhancing":[145],"instance-level":[146],"semantic":[147],"representations":[148],"Class-Conditioned":[150],"Instance-Adaptive":[152],"Prompts.":[153],"Experiments":[154],"across":[155],"six":[156],"diverse":[157],"demonstrate":[159],"COIN\u2019s":[160],"robustness":[161],"generalization.":[163],"Code":[164],"available":[166],"at":[167],"https://github.com/imhgy/COIN.":[168]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-05-06T00:00:00"}
