{"id":"https://openalex.org/W4411408309","doi":"https://doi.org/10.1109/tnnls.2025.3578618","title":"DashFusion: Dual-Stream Alignment With Hierarchical Bottleneck Fusion for Multimodal Sentiment Analysis","display_name":"DashFusion: Dual-Stream Alignment With Hierarchical Bottleneck Fusion for Multimodal Sentiment Analysis","publication_year":2025,"publication_date":"2025-06-18","ids":{"openalex":"https://openalex.org/W4411408309","doi":"https://doi.org/10.1109/tnnls.2025.3578618","pmid":"https://pubmed.ncbi.nlm.nih.gov/40531640"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2025.3578618","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3578618","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5065822391","display_name":"Yuhua Wen","orcid":"https://orcid.org/0009-0006-7059-9196"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhua Wen","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0006-7059-9196","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101596032","display_name":"Qifei Li","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qifei Li","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5016-0094","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100681370","display_name":"Yingying Zhou","orcid":"https://orcid.org/0009-0001-5217-389X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingying Zhou","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033063238","display_name":"Yingming Gao","orcid":"https://orcid.org/0000-0001-5881-3723"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingming Gao","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5881-3723","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111223590","display_name":"Zhengqi Wen","orcid":"https://orcid.org/0000-0001-9430-7115"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengqi Wen","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112613657","display_name":"Jianhua Tao","orcid":"https://orcid.org/0000-0002-9344-6428"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhua Tao","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9344-6428","affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100343662","display_name":"Ya Li","orcid":"https://orcid.org/0000-0002-6284-5039"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ya Li","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6284-5039","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.188,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.93872402,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"36","issue":"10","first_page":"17941","last_page":"17952"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9979000091552734,"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"}},"topics":[{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9979000091552734,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9800999760627747,"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/T11309","display_name":"Music and Audio Processing","score":0.9765999913215637,"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":0.8306733965873718},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.7574621438980103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5531389713287354},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.482001930475235},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.4354574382305145},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.4226577579975128},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3547285795211792},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33118736743927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8306733965873718},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7574621438980103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5531389713287354},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.482001930475235},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.4354574382305145},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.4226577579975128},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3547285795211792},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33118736743927},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2025.3578618","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3578618","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:40531640","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40531640","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6905454263","display_name":null,"funder_award_id":"2024YFB2808802","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W2095176743","https://openalex.org/W2191779130","https://openalex.org/W2194775991","https://openalex.org/W2290844432","https://openalex.org/W2556418146","https://openalex.org/W2619383789","https://openalex.org/W2798991696","https://openalex.org/W2807126412","https://openalex.org/W2883409523","https://openalex.org/W2888192920","https://openalex.org/W2964010806","https://openalex.org/W2964051877","https://openalex.org/W2964346351","https://openalex.org/W3015123307","https://openalex.org/W3016026271","https://openalex.org/W3034266838","https://openalex.org/W3034849760","https://openalex.org/W3035524453","https://openalex.org/W3093051361","https://openalex.org/W3128412859","https://openalex.org/W3169801598","https://openalex.org/W3197875592","https://openalex.org/W3214432797","https://openalex.org/W4221155339","https://openalex.org/W4285184319","https://openalex.org/W4297499129","https://openalex.org/W4304091726","https://openalex.org/W4304092664","https://openalex.org/W4321793515","https://openalex.org/W4322765588","https://openalex.org/W4362714356","https://openalex.org/W4376455521","https://openalex.org/W4380875665","https://openalex.org/W4385064532","https://openalex.org/W4385570923","https://openalex.org/W4385573848","https://openalex.org/W4386075879","https://openalex.org/W4388188113","https://openalex.org/W4391853581","https://openalex.org/W4394606408","https://openalex.org/W4400111734","https://openalex.org/W4400350678","https://openalex.org/W4400900379","https://openalex.org/W4402112290","https://openalex.org/W4402704633"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W1973775000","https://openalex.org/W2748922771","https://openalex.org/W1987128138","https://openalex.org/W2743976221"],"abstract_inverted_index":{"Multimodal":[0],"sentiment":[1],"analysis":[2],"(MSA)":[3],"integrates":[4,141],"various":[5,177],"modalities,":[6,40],"such":[7],"as":[8],"text,":[9],"image,":[10],"and":[11,28,36,66,95,154,165,179,188],"audio,":[12],"to":[13,62,104,130],"provide":[14],"a":[15,49,74,150],"more":[16],"comprehensive":[17],"understanding":[18],"of":[19,185],"sentiment.":[20],"However,":[21],"effective":[22],"MSA":[23],"is":[24],"challenged":[25],"by":[26],"alignment":[27,56,79,88,99,112,187],"fusion":[29,42,58,83,138,189],"issues.":[30],"Alignment":[31],"requires":[32],"synchronizing":[33],"both":[34],"temporal":[35,94],"semantic":[37,96],"information":[38,129,143],"across":[39,115,176],"while":[41],"involves":[43],"integrating":[44],"these":[45,70],"aligned":[46],"features":[47,92],"into":[48],"unified":[50],"representation.":[51],"Existing":[52],"methods":[53],"often":[54],"address":[55],"or":[57],"in":[59,64],"isolation,":[60],"leading":[61],"limitations":[63],"performance":[65,153,175],"efficiency.":[67,156],"To":[68],"tackle":[69],"issues,":[71],"we":[72],"propose":[73],"novel":[75],"framework":[76],"called":[77],"dual-stream":[78,87],"with":[80],"hierarchical":[81,136],"bottleneck":[82,137,146],"(DashFusion).":[84],"First,":[85],"the":[86,116,132,183],"module":[89],"synchronizes":[90],"multimodal":[91,109,142],"through":[93,119,144],"alignment.":[97],"Temporal":[98],"employs":[100],"cross-modal":[101],"attention":[102],"(CA)":[103],"establish":[105],"frame-level":[106],"correspondences":[107],"among":[108],"sequences.":[110],"Semantic":[111],"ensures":[113],"consistency":[114],"feature":[117],"space":[118],"contrastive":[120,124],"learning.":[121],"Second,":[122],"supervised":[123],"learning":[125],"(SCL)":[126],"leverages":[127],"label":[128],"refine":[131],"modality":[133],"features.":[134],"Finally,":[135],"(HBF)":[139],"progressively":[140],"compressed":[145],"tokens,":[147],"which":[148],"achieves":[149,172],"balance":[151],"between":[152],"computational":[155],"We":[157],"evaluate":[158],"DashFusion":[159,171],"on":[160],"three":[161],"datasets:":[162],"CMU-MOSI,":[163],"CMU-MOSEI,":[164],"CH-SIMS.":[166],"Experimental":[167],"results":[168],"demonstrate":[169],"that":[170],"state-of-the-art":[173],"(SOTA)":[174],"metrics,":[178],"ablation":[180],"studies":[181],"confirm":[182],"effectiveness":[184],"our":[186,194],"techniques.":[190],"The":[191],"codes":[192],"for":[193],"experiments":[195],"are":[196],"available":[197],"at":[198],"https://github.com/ultramarineX/DashFusion.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2025-12-10T02:45:41.426853","created_date":"2025-10-10T00:00:00"}