{"id":"https://openalex.org/W4388188113","doi":"https://doi.org/10.1145/3581783.3611974","title":"AcFormer: An Aligned and Compact Transformer for Multimodal Sentiment Analysis","display_name":"AcFormer: An Aligned and Compact Transformer for Multimodal Sentiment Analysis","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4388188113","doi":"https://doi.org/10.1145/3581783.3611974"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3611974","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3611974","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","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/A5004411500","display_name":"Daoming Zong","orcid":"https://orcid.org/0009-0004-8109-2943"},"institutions":[{"id":"https://openalex.org/I4210128910","display_name":"Group Sense (China)","ror":"https://ror.org/036wd5777","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210128910"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Daoming Zong","raw_affiliation_strings":["SenseTime Group Limited, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-8109-2943","affiliations":[{"raw_affiliation_string":"SenseTime Group Limited, Beijing, China","institution_ids":["https://openalex.org/I4210128910"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032101339","display_name":"Chaoyue Ding","orcid":"https://orcid.org/0009-0000-0161-4838"},"institutions":[{"id":"https://openalex.org/I4210128910","display_name":"Group Sense (China)","ror":"https://ror.org/036wd5777","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210128910"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaoyue Ding","raw_affiliation_strings":["SenseTime Group Limited, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-0161-4838","affiliations":[{"raw_affiliation_string":"SenseTime Group Limited, Beijing, China","institution_ids":["https://openalex.org/I4210128910"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003607066","display_name":"Baoxiang Li","orcid":"https://orcid.org/0009-0009-4490-2157"},"institutions":[{"id":"https://openalex.org/I4210128910","display_name":"Group Sense (China)","ror":"https://ror.org/036wd5777","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210128910"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoxiang Li","raw_affiliation_strings":["SenseTime Group Limited, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-4490-2157","affiliations":[{"raw_affiliation_string":"SenseTime Group Limited, Beijing, China","institution_ids":["https://openalex.org/I4210128910"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiakui Li","orcid":"https://orcid.org/0009-0004-2492-3528"},"institutions":[{"id":"https://openalex.org/I4210128910","display_name":"Group Sense (China)","ror":"https://ror.org/036wd5777","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210128910"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiakui Li","raw_affiliation_strings":["SenseTime Group Limited, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-2492-3528","affiliations":[{"raw_affiliation_string":"SenseTime Group Limited, Beijing, China","institution_ids":["https://openalex.org/I4210128910"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080751407","display_name":"Keru Zheng","orcid":"https://orcid.org/0009-0001-2374-8682"},"institutions":[{"id":"https://openalex.org/I4210128910","display_name":"Group Sense (China)","ror":"https://ror.org/036wd5777","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210128910"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ken Zheng","raw_affiliation_strings":["SenseTime Group Limited, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0001-2374-8682","affiliations":[{"raw_affiliation_string":"SenseTime Group Limited, Beijing, China","institution_ids":["https://openalex.org/I4210128910"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072828732","display_name":"Qunyan Zhou","orcid":"https://orcid.org/0009-0007-0746-9249"},"institutions":[{"id":"https://openalex.org/I4210128910","display_name":"Group Sense (China)","ror":"https://ror.org/036wd5777","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210128910"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qunyan Zhou","raw_affiliation_strings":["SenseTime Group Limited, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-0746-9249","affiliations":[{"raw_affiliation_string":"SenseTime Group Limited, Beijing, China","institution_ids":["https://openalex.org/I4210128910"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5004411500"],"corresponding_institution_ids":["https://openalex.org/I4210128910"],"apc_list":null,"apc_paid":null,"fwci":5.1831,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.96009266,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"833","last_page":"842"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9980000257492065,"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":0.9980000257492065,"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/T11795","display_name":"Humor Studies and Applications","score":0.9957000017166138,"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"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.7737511992454529},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.68021160364151},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.583488941192627},{"id":"https://openalex.org/keywords/multimodality","display_name":"Multimodality","score":0.5471107959747314},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5387528538703918},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.49432849884033203},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.48642444610595703},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4529232084751129},{"id":"https://openalex.org/keywords/multimodal-learning","display_name":"Multimodal learning","score":0.44713470339775085},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.4456925690174103},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.44502872228622437},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4196474850177765},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4009195566177368},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22978997230529785}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7737511992454529},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.68021160364151},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.583488941192627},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.5471107959747314},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5387528538703918},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.49432849884033203},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.48642444610595703},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4529232084751129},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.44713470339775085},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.4456925690174103},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.44502872228622437},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4196474850177765},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4009195566177368},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22978997230529785},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","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},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","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/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3611974","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3611974","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","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":34,"referenced_works":["https://openalex.org/W1595126664","https://openalex.org/W2016730668","https://openalex.org/W2095176743","https://openalex.org/W2146334809","https://openalex.org/W2187089797","https://openalex.org/W2251394420","https://openalex.org/W2395639500","https://openalex.org/W2519656895","https://openalex.org/W2556418146","https://openalex.org/W2619383789","https://openalex.org/W2767249564","https://openalex.org/W2936774411","https://openalex.org/W2949391930","https://openalex.org/W2958722525","https://openalex.org/W2962931510","https://openalex.org/W2964010806","https://openalex.org/W2964216663","https://openalex.org/W2997573100","https://openalex.org/W3036601975","https://openalex.org/W3037572520","https://openalex.org/W3093051361","https://openalex.org/W3163841364","https://openalex.org/W3169801598","https://openalex.org/W3173549566","https://openalex.org/W3174906557","https://openalex.org/W3184735396","https://openalex.org/W4210827551","https://openalex.org/W4288804239","https://openalex.org/W4304091726","https://openalex.org/W4304092664","https://openalex.org/W4376455521","https://openalex.org/W6685518012","https://openalex.org/W6797613833","https://openalex.org/W6844956273"],"related_works":["https://openalex.org/W4236665645","https://openalex.org/W3093803775","https://openalex.org/W4381827277","https://openalex.org/W3157841754","https://openalex.org/W4390136517","https://openalex.org/W2563212008","https://openalex.org/W4399869253","https://openalex.org/W3013953798","https://openalex.org/W2477990774","https://openalex.org/W3167558523"],"abstract_inverted_index":{"Multimodal":[0],"Sentiment":[1],"Analysis":[2],"(MSA)":[3],"is":[4,25,165],"a":[5,122],"popular":[6],"research":[7],"topic":[8],"aimed":[9],"at":[10,168],"utilizing":[11],"multimodal":[12,87,137],"signals":[13],"for":[14,49,86],"understanding":[15],"human":[16],"emotions.":[17],"The":[18,89,105],"primary":[19],"approach":[20],"to":[21,26,42,74,96,102,121,159],"solving":[22],"this":[23],"task":[24],"develop":[27],"complex":[28],"fusion":[29,126],"techniques.":[30],"However,":[31],"the":[32,50,151],"heterogeneity":[33],"and":[34,71,82,117,142],"unaligned":[35],"nature":[36],"between":[37],"modalities":[38,73],"pose":[39],"significant":[40],"challenges":[41],"fusion.":[43,54],"Additionally,":[44],"existing":[45],"methods":[46],"lack":[47],"consideration":[48],"efficiency":[51],"of":[52,94,125],"modal":[53],"To":[55],"tackle":[56],"these":[57],"issues,":[58],"we":[59],"propose":[60],"AcFormer,":[61],"which":[62],"contains":[63],"two":[64],"core":[65],"ingredients:":[66],"i)":[67],"contrastive":[68],"learning":[69],"within":[70],"across":[72],"explicitly":[75],"align":[76],"different":[77],"modality":[78],"streams":[79],"before":[80],"fusion;":[81],"ii)":[83],"pivot":[84,127],"attention":[85,108,120],"interaction/fusion.":[88],"former":[90],"encourages":[91],"positive":[92],"triplets":[93],"image-audio-text":[95],"have":[97],"similar":[98],"representations":[99],"in":[100],"contrast":[101],"negative":[103],"ones.":[104],"latter":[106],"introduces":[107],"pivots":[109],"that":[110,148],"can":[111],"serve":[112],"as":[113],"cross-modal":[114,119],"information":[115],"bridges":[116],"limit":[118],"certain":[123],"number":[124],"tokens.":[128],"We":[129],"evaluate":[130],"AcFormer":[131,149],"on":[132],"multiple":[133],"MSA":[134],"tasks,":[135],"including":[136],"emotion":[138],"recognition,":[139],"humor":[140],"detection,":[141],"sarcasm":[143],"detection.":[144],"Empirical":[145],"evidence":[146],"shows":[147],"achieves":[150],"optimal":[152],"performance":[153],"with":[154],"minimal":[155],"computation":[156],"cost":[157],"compared":[158],"previous":[160],"state-of-the-art":[161],"methods.":[162],"Our":[163],"code":[164],"publicly":[166],"available":[167],"https://github.com/dingchaoyue/AcFormer.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":5}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}