{"id":"https://openalex.org/W7164033727","doi":"https://doi.org/10.48550/arxiv.2606.07935","title":"REACT 2026: The Fourth Multiple Appropriate Facial Reaction Generation Challenge: Personalised MAFRG and Appropriate EEG Reaction Prediction","display_name":"REACT 2026: The Fourth Multiple Appropriate Facial Reaction Generation Challenge: Personalised MAFRG and Appropriate EEG Reaction Prediction","publication_year":2026,"publication_date":"2026-06-06","ids":{"openalex":"https://openalex.org/W7164033727","doi":"https://doi.org/10.48550/arxiv.2606.07935"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.07935","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07935","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","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.2606.07935","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138207022","display_name":"Siyang Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Siyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036456924","display_name":"Micol Spitale","orcid":"https://orcid.org/0000-0002-3418-1933"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Spitale, Micol","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122957980","display_name":"Zijian Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zijian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138268644","display_name":"Xiangyu Kong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kong, Xiangyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138269716","display_name":"Cheng Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056906303","display_name":"Cristina Palmero","orcid":"https://orcid.org/0000-0002-6085-6527"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Palmero, Cristina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030892971","display_name":"Germ\u00e1n Barquero","orcid":"https://orcid.org/0000-0001-8381-3549"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barquero, German","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138224589","display_name":"Sergio Escalera","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Escalera, Sergio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137121471","display_name":"Michel Valstar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Valstar, Michel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138285040","display_name":"Mohamed Daoudi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daoudi, Mohamed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138277256","display_name":"Fabien Ringeval","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ringeval, Fabien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109361451","display_name":"Andrew Howes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Howes, Andrew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138259146","display_name":"Elisabeth Andre","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andre, Elisabeth","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138232070","display_name":"Hatice Gunes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gunes, Hatice","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":0.8790000081062317,"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":0.8790000081062317,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.011599999852478504,"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"}},{"id":"https://openalex.org/T11519","display_name":"Digital Mental Health Interventions","score":0.011500000022351742,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied 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/facial-expression","display_name":"Facial expression","score":0.5935999751091003},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.4957999885082245},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.39100000262260437},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.38370001316070557},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3707999885082245},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.37059998512268066}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7056999802589417},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.5935999751091003},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5253999829292297},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.4957999885082245},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4629000127315521},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.39100000262260437},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.38370001316070557},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3707999885082245},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.33000001311302185},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3034000098705292},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2768999934196472},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.25949999690055847}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.07935","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07935","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.07935","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07935","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.46259933710098267,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"dyadic":[1,147],"interactions,":[2],"various":[3],"human":[4,14,86,134],"facial":[5,47,80,129],"reactions":[6,81],"could":[7],"be":[8],"appropriate":[9,46],"for":[10,41,88],"responding":[11,89],"to":[12,90],"each":[13,91],"speaker":[15,93],"behaviour.":[16,94],"Following":[17],"the":[18,22,42,55,60,99,154,161],"successful":[19],"organisation":[20],"of":[21,32,44,64,98],"REACT":[23,56,111],"2023,":[24],"2024":[25],"and":[26,62,77,120,138,157,167,175],"2025":[27,112],"challenge":[28,58,104,155],"series,":[29],"a":[30,84,96,125],"body":[31],"generative":[33],"deep":[34],"learning":[35],"(DL)":[36],"models":[37,68],"have":[38],"been":[39],"developed":[40],"problem":[43],"multiple":[45,72],"reaction":[48,130],"generation":[49,131],"(MAFRG).":[50],"This":[51,123,150],"year,":[52],"we":[53,101],"propose":[54],"2026":[57],"encouraging":[59],"development":[61],"benchmarking":[63],"Machine":[65],"Learning":[66],"(ML)":[67],"that":[69],"can":[70],"generate":[71],"personalised,":[73],"appropriate,":[74],"diverse,":[75],"realistic":[76],"synchronised":[78],"human-style":[79],"expressed":[82],"by":[83,110],"specific":[85],"listener":[87],"given":[92],"As":[95],"key":[97],"challenge,":[100],"continuously":[102],"provide":[103,115],"participants":[105],"with":[106],"MARS":[107],"dataset":[108],"introduced":[109],"but":[113],"additionally":[114],"individual-level":[116],"Big-Five":[117],"personality":[118],"labels":[119],"EEG":[121],"recordings.":[122],"introduces":[124],"new":[126,158],"one-to-many":[127],"personalised":[128,168,176],"setting":[132],"combining":[133],"expressive":[135],"behavioural,":[136],"affective":[137],"neurophysiological":[139],"signals,":[140],"which":[141,179],"remains":[142],"largely":[143],"unexplored":[144],"in":[145],"current":[146],"interaction":[148],"modelling.":[149],"paper":[151],"also":[152],"presents":[153],"guidelines":[156],"baselines":[159],"on":[160],"four":[162],"proposed":[163],"sub-challenges:":[164],"Offline":[165],"generic":[166,174],"MAFRG":[169],"as":[170,172],"well":[171],"Online":[173],"MAFRG,":[177],"respectively,":[178],"are":[180],"publicly":[181],"available":[182],"at":[183],"https://github.com/reactmultimodalchallenge/baseline_react2026.":[184]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}