{"id":"https://openalex.org/W7204809865","doi":"https://doi.org/10.1080/08839514.2026.2721303","title":"Lightweight Multiple Appropriate Facial Reaction Generation: Pruning-Regeneration and Correction Hybrid Precision Quantization","display_name":"Lightweight Multiple Appropriate Facial Reaction Generation: Pruning-Regeneration and Correction Hybrid Precision Quantization","publication_year":2026,"publication_date":"2026-08-30","ids":{"openalex":"https://openalex.org/W7204809865","doi":"https://doi.org/10.1080/08839514.2026.2721303"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2026.2721303","doi":"https://doi.org/10.1080/08839514.2026.2721303","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2026.2721303","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2026.2721303?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","dongbi-c","jufo-1","ki-jl-1","norway-1","oarl","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2026.2721303?needAccess=true","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057917536","display_name":"Jiayan Gu","orcid":"https://orcid.org/0000-0001-9355-5395","observed_orcids":["https://orcid.org/0000-0001-9355-5395"]},"institutions":[{"id":"https://openalex.org/I39774598","display_name":"Hefei University","ror":"https://ror.org/01f5rdf64","country_code":"CN","type":"education","lineage":["https://openalex.org/I39774598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayan Gu","raw_affiliation_strings":["Hefei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hefei University","institution_ids":["https://openalex.org/I39774598"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017746798","display_name":"Junkai Ding","orcid":"https://orcid.org/0009-0009-0880-244X","observed_orcids":["https://orcid.org/0009-0009-0880-244X"]},"institutions":[{"id":"https://openalex.org/I39774598","display_name":"Hefei University","ror":"https://ror.org/01f5rdf64","country_code":"CN","type":"education","lineage":["https://openalex.org/I39774598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junkai Ding","raw_affiliation_strings":["Hefei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hefei University","institution_ids":["https://openalex.org/I39774598"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134129492","display_name":"Siyang Song","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Siyang Song","raw_affiliation_strings":["University of Exeter"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Exeter","institution_ids":["https://openalex.org/I23923803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113987202","display_name":"Haoxiang Huang","orcid":"https://orcid.org/0009-0007-3354-4425","observed_orcids":["https://orcid.org/0009-0007-3354-4425"]},"institutions":[{"id":"https://openalex.org/I7882870","display_name":"University of Glasgow","ror":"https://ror.org/00vtgdb53","country_code":"GB","type":"education","lineage":["https://openalex.org/I7882870"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Haoxiang Huang","raw_affiliation_strings":["University of Glasgow"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Glasgow","institution_ids":["https://openalex.org/I7882870"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071939530","display_name":"Chuanru Ren","orcid":"https://orcid.org/0000-0002-2708-1491","observed_orcids":["https://orcid.org/0000-0002-2708-1491"]},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuanru Ren","raw_affiliation_strings":["Tongji University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongji University","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5138202214","display_name":"Shengbing Chen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I39774598","display_name":"Hefei University","ror":"https://ror.org/01f5rdf64","country_code":"CN","type":"education","lineage":["https://openalex.org/I39774598"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shengbing Chen","raw_affiliation_strings":["Hefei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hefei University","institution_ids":["https://openalex.org/I39774598"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5138202214"],"corresponding_institution_ids":["https://openalex.org/I39774598"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":{"value":2195,"currency":"USD","value_usd":2195},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8008000254631042,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.8008000254631042,"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/T11448","display_name":"Face recognition and analysis","score":0.0778999999165535,"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":0.0640999972820282,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/facial-expression-synthesis","display_name":"facial expression synthesis","score":0.6729999780654907},{"id":"https://openalex.org/keywords/lightweight-neural-networks","display_name":"lightweight neural networks","score":0.7080000042915344},{"id":"https://openalex.org/keywords/adaptive-thresholding","display_name":"adaptive thresholding","score":0.6710000038146973},{"id":"https://openalex.org/keywords/low-bit-quantization","display_name":"low-bit quantization","score":0.7630000114440918},{"id":"https://openalex.org/keywords/bit-width-allocation","display_name":"bit-width allocation","score":0.5490000247955322},{"id":"https://openalex.org/keywords/semantic-drift","display_name":"semantic drift","score":0.746999979019165},{"id":"https://openalex.org/keywords/human-computer-interaction","display_name":"human-computer interaction","score":0.8309999704360962}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8946999907493591},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5509999990463257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5175999999046326},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.450300008058548},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.42239999771118164},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.39500001072883606},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.3887999951839447},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.34450000524520874},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30469998717308044},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28839999437332153},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.2872999906539917},{"id":"https://openalex.org/C90559484","wikidata":"https://www.wikidata.org/wiki/Q778379","display_name":"Expression (computer science)","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/08839514.2026.2721303","doi":"https://doi.org/10.1080/08839514.2026.2721303","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2026.2721303","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2026.2721303?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","dongbi-c","jufo-1","ki-jl-1","norway-1","oarl","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b61254ecc7204cf787d73855c335589c","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/b61254ecc7204cf787d73855c335589c","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 40, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1080/08839514.2026.2721303","doi":"https://doi.org/10.1080/08839514.2026.2721303","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2026.2721303","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2026.2721303?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","dongbi-c","jufo-1","ki-jl-1","norway-1","oarl","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204809865.pdf","grobid_xml":"https://content.openalex.org/works/W7204809865.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1487062357","https://openalex.org/W1974643247","https://openalex.org/W2276892413","https://openalex.org/W2740440254","https://openalex.org/W2787512630","https://openalex.org/W2963363373","https://openalex.org/W3034513523","https://openalex.org/W4321011462","https://openalex.org/W4378474373","https://openalex.org/W4382466739","https://openalex.org/W4386065441","https://openalex.org/W4386065704","https://openalex.org/W4386076113","https://openalex.org/W4387968034","https://openalex.org/W4387968147","https://openalex.org/W4387969719","https://openalex.org/W4388188050","https://openalex.org/W4390871868","https://openalex.org/W4390873610","https://openalex.org/W4393157004","https://openalex.org/W4399115604","https://openalex.org/W4400527597","https://openalex.org/W4402715871","https://openalex.org/W4402753755","https://openalex.org/W4403792418","https://openalex.org/W4404480514","https://openalex.org/W4408564878","https://openalex.org/W4409346650","https://openalex.org/W4410616025","https://openalex.org/W4412945475","https://openalex.org/W4415535552","https://openalex.org/W4415796210","https://openalex.org/W4415797160","https://openalex.org/W4416016523","https://openalex.org/W7018794704","https://openalex.org/W7122395680","https://openalex.org/W7133227811","https://openalex.org/W7133241261"],"related_works":["https://openalex.org/W4406462544","https://openalex.org/W7172481784","https://openalex.org/W7167715405","https://openalex.org/W4387677857","https://openalex.org/W4406232108","https://openalex.org/W4313011785","https://openalex.org/W4405180247","https://openalex.org/W4396775596","https://openalex.org/W4378474373","https://openalex.org/W4362585722"],"abstract_inverted_index":{"Advances":[0],"in":[1,14,154],"artificial":[2],"intelligence":[3],"have":[4],"made":[5],"Multiple":[6],"Appropriate":[7],"Facial":[8],"Reaction":[9],"Generation":[10],"(MAFRG)":[11],"increasingly":[12],"important":[13],"human-computer":[15],"interaction.":[16],"However,":[17],"existing":[18],"models":[19],"face":[20],"practical":[21],"deployment":[22,166],"challenges":[23],"due":[24],"to":[25,69,115],"complex":[26],"structures":[27],"and":[28,51,64,84,119,137,143],"high":[29],"computational":[30],"costs.":[31],"This":[32],"paper":[33],"proposes":[34],"a":[35,65,105,111,160],"lightweight":[36,165],"framework":[37],"for":[38],"generating":[39],"appropriate":[40],"multimodal":[41],"facial":[42,77],"reactions,":[43],"comprising":[44],"two":[45],"complementary":[46],"methods":[47],"targeting":[48],"structural":[49],"sparsity":[50],"numerical":[52],"precision,":[53],"respectively.":[54],"DyPR-Net":[55],"dynamically":[56],"adjusts":[57],"its":[58],"sparse":[59],"structure":[60],"via":[61],"adaptive":[62],"thresholding":[63],"weight":[66],"regeneration":[67],"mechanism":[68],"restore":[70],"critical":[71],"connections,":[72],"balancing":[73],"compression":[74],"efficiency":[75],"with":[76,110],"reaction":[78],"fidelity.":[79],"GOMD-Net":[80],"addresses":[81],"semantic":[82],"drift":[83],"degraded":[85],"expression":[86],"quality":[87],"under":[88,123],"low-bit":[89],"quantization":[90],"by":[91],"adaptively":[92],"allocating":[93],"bit":[94],"widths":[95],"across":[96,130],"layers":[97],"based":[98],"on":[99,134],"inter-layer":[100],"orthogonality.":[101],"It":[102],"further":[103],"employs":[104],"Distribution-Skew-Correction":[106],"(DSC)":[107],"strategy":[108],"combined":[109],"global":[112],"optimization":[113],"objective":[114],"faithfully":[116],"reproduce":[117],"subtle":[118],"diverse":[120],"emotional":[121],"expressions":[122],"extremely":[124],"low":[125],"bit-width":[126],"constraints.":[127],"Extensive":[128],"experiments":[129],"multiple":[131],"mainstream":[132],"architectures":[133],"the":[135,141,147,155,164],"NoXi":[136],"RECOLA":[138],"datasets":[139],"demonstrate":[140],"effectiveness":[142],"potential":[144],"generalizability":[145],"of":[146,167],"proposed":[148],"framework,":[149],"reducing":[150],"model":[151],"size,":[152],"FLOPs":[153],"tested":[156],"environment,":[157],"thereby":[158],"offering":[159],"promising":[161],"basis":[162],"toward":[163],"MAFRG":[168],"models.":[169]},"counts_by_year":[],"updated_date":"2026-11-13T13:42:25.865203","created_date":"2026-09-01T00:00:00"}