{"id":"https://openalex.org/W7162073518","doi":"https://doi.org/10.48550/arxiv.2605.21869","title":"Two-Stage Multimodal Framework for Emotion Mimicry Intensity Prediction","display_name":"Two-Stage Multimodal Framework for Emotion Mimicry Intensity Prediction","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162073518","doi":"https://doi.org/10.48550/arxiv.2605.21869"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21869","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.21869","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136743775","display_name":"Dinithi Dissanayake","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dissanayake, Dinithi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133451510","display_name":"Shaveen Silva","orcid":"https://orcid.org/0009-0002-3485-7558"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Silva, Shaveen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133451951","display_name":"Ovindu Atukorala","orcid":"https://orcid.org/0009-0009-7411-125X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Atukorala, Ovindu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017066505","display_name":"Prasanth Sasikumar","orcid":"https://orcid.org/0000-0002-5844-9164"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sasikumar, Prasanth","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135686464","display_name":"Suranga Nanayakkara","orcid":"https://orcid.org/0000-0001-7441-5493"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nanayakkara, Suranga","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9801999926567078,"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":0.9801999926567078,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.002400000113993883,"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"}},{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.0017000000225380063,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social 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/correlation","display_name":"Correlation","score":0.5652999877929688},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.5085999965667725},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4943000078201294},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.48899999260902405},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4487000107765198},{"id":"https://openalex.org/keywords/mimicry","display_name":"Mimicry","score":0.4465000033378601},{"id":"https://openalex.org/keywords/intensity","display_name":"Intensity (physics)","score":0.3772999942302704}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6657999753952026},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5680000185966492},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5652999877929688},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.5085999965667725},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4943000078201294},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.48899999260902405},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4487000107765198},{"id":"https://openalex.org/C7863114","wikidata":"https://www.wikidata.org/wiki/Q192627","display_name":"Mimicry","level":2,"score":0.4465000033378601},{"id":"https://openalex.org/C93038891","wikidata":"https://www.wikidata.org/wiki/Q1061524","display_name":"Intensity (physics)","level":2,"score":0.3772999942302704},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35899999737739563},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3151000142097473},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29010000824928284},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21869","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.21869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21869","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,34],"present":[1],"our":[2,77],"submission":[3],"to":[4,14,117],"the":[5,80,87,92,104,125,136],"Hume-ABAW10":[6],"Emotional":[7],"Mimicry":[8],"Intensity":[9],"(EMI)":[10],"Challenge,":[11],"which":[12],"aims":[13],"predict":[15],"six":[16],"continuous":[17],"emotion":[18],"intensity":[19],"dimensions:":[20],"Admiration,":[21],"Amusement,":[22],"Determination,":[23],"Empathic":[24],"Pain,":[25],"Excitement,":[26],"and":[27,44,59,72,144],"Joy,":[28],"from":[29],"in-the-wild":[30],"multimodal":[31,38],"video":[32],"clips.":[33],"propose":[35],"a":[36,66,142],"staged":[37],"framework":[39],"that":[40],"combines":[41],"textual,":[42],"acoustic,":[43],"visual":[45],"representations,":[46],"with":[47,69],"an":[48,97,129],"optional":[49],"motion":[50,105],"branch.":[51],"Our":[52,119],"approach":[53],"first":[54],"trains":[55],"modality-specific":[56],"encoders":[57],"independently":[58],"then":[60],"fuses":[61],"their":[62],"learned":[63],"representations":[64],"through":[65],"lightweight":[67],"regressor":[68],"modality":[70],"dropout":[71],"controlled":[73],"encoder":[74],"adaptation.":[75],"Across":[76],"submitted":[78],"systems,":[79],"best":[81],"validation":[82],"performance":[83],"is":[84],"obtained":[85],"by":[86],"text--audio--vision--motion":[88],"fusion":[89],"model":[90],"under":[91],"expanded":[93],"4:1":[94],"split,":[95],"achieving":[96,128],"average":[98,130],"Pearson":[99,131],"correlation":[100,132],"of":[101,133],"0.4722.":[102],"Although":[103],"branch":[106],"yields":[107],"only":[108],"very":[109],"slight":[110],"gains,":[111],"its":[112],"behavior":[113],"can":[114],"be":[115],"interesting":[116],"study.":[118],"team":[120],"was":[121],"placed":[122],"third":[123],"in":[124],"EMI":[126,148],"challenge,":[127],"0.57":[134],"for":[135,147],"test":[137],"set.":[138],"Overall,":[139],"we":[140],"provide":[141],"practical":[143],"reproducible":[145],"baseline":[146],"prediction.":[149]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-23T00:00:00"}
