{"id":"https://openalex.org/W7138361932","doi":"https://doi.org/10.1609/aaai.v40i25.39248","title":"To Align or Not to Align: Strategic Multimodal Representation Alignment for Optimal Performance","display_name":"To Align or Not to Align: Strategic Multimodal Representation Alignment for Optimal Performance","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138361932","doi":"https://doi.org/10.1609/aaai.v40i25.39248"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i25.39248","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i25.39248","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i25.39248","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113067511","display_name":"Wanlong Fang","orcid":"https://orcid.org/0000-0003-4480-3107"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wanlong Fang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129686025","display_name":"Tianle Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tianle Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129644695","display_name":"Alvin Chan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alvin Chan","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":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"25","first_page":"21056","last_page":"21064"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.12110000103712082,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.12110000103712082,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0746999979019165,"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":0.06080000102519989,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6858999729156494},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5437999963760376},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5317000150680542},{"id":"https://openalex.org/keywords/strategic-alignment","display_name":"Strategic alignment","score":0.4871000051498413},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.41850000619888306},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.384799987077713},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.36719998717308044}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7354000210762024},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6858999729156494},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5437999963760376},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5317000150680542},{"id":"https://openalex.org/C129011618","wikidata":"https://www.wikidata.org/wiki/Q7621821","display_name":"Strategic alignment","level":4,"score":0.4871000051498413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4650999903678894},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.41850000619888306},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.384799987077713},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.36719998717308044},{"id":"https://openalex.org/C4668613","wikidata":"https://www.wikidata.org/wiki/Q4116110","display_name":"Structural alignment","level":5,"score":0.36059999465942383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35190001130104065},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3492000102996826},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2655999958515167},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i25.39248","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i25.39248","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:dr.ntu.edu.sg:10356/210686","is_oa":false,"landing_page_url":"https://hdl.handle.net/10356/210686","pdf_url":null,"source":{"id":"https://openalex.org/S4306402609","display_name":"DR-NTU (Nanyang Technological University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I172675005","host_organization_name":"Nanyang Technological University","host_organization_lineage":["https://openalex.org/I172675005"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Conference Paper"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i25.39248","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i25.39248","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"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":{"Multimodal":[0],"learning":[1,85],"often":[2],"relies":[3],"on":[4,109,125,145,178],"aligning":[5],"representations":[6,55],"across":[7],"modalities":[8],"to":[9,17,98,133,182],"enable":[10],"effective":[11],"information":[12,77,171],"integration\u2014an":[13],"approach":[14],"traditionally":[15],"assumed":[16],"be":[18],"universally":[19],"beneficial.":[20],"However,":[21],"prior":[22],"research":[23],"has":[24],"primarily":[25],"taken":[26],"an":[27,157],"observational":[28],"approach,":[29],"examining":[30],"naturally":[31],"occurring":[32],"alignment":[33,53,66,73,92,102,124,143,159,184],"in":[34,168],"multimodal":[35],"data":[36,116],"and":[37,71,111,165,180],"exploring":[38],"its":[39],"correlation":[40],"with":[41],"model":[42,69],"performance,":[43],"without":[44],"systematically":[45],"studying":[46],"the":[47,120,126,134,137,139,146,151,169],"direct":[48],"effects":[49],"of":[50,56,91,122,128,136,142,148],"explicitly":[51],"enforced":[52],"between":[54,150],"different":[57,75,115,152],"modalities.":[58,153],"In":[59],"this":[60],"work,":[61],"we":[62,80],"investigate":[63],"how":[64,181],"explicit":[65,101,123],"influences":[67],"both":[68],"performance":[70,127],"representation":[72],"under":[74,114],"modality-specific":[76,163],"structures.":[78],"Specifically,":[79],"introduce":[81],"a":[82],"controllable":[83],"contrastive":[84],"module":[86],"that":[87,119,161],"enables":[88],"precise":[89],"manipulation":[90],"strength":[93,160],"during":[94],"training,":[95],"allowing":[96],"us":[97],"explore":[99],"when":[100,179],"improves":[103],"or":[104],"hinders":[105],"performance.":[106,189],"Our":[107],"results":[108],"synthetic":[110],"real":[112],"datasets":[113],"characteristics":[117,135],"show":[118],"impact":[121],"unimodal":[129,187],"models":[130],"is":[131],"related":[132],"data:":[138],"optimal":[140,158,186],"level":[141],"depends":[144],"amount":[147],"redundancy":[149,167],"We":[154],"can":[155,175],"find":[156],"balances":[162],"signals":[164],"shared":[166],"mixed":[170],"distributions.":[172],"This":[173],"work":[174],"help":[176],"practitioners":[177],"enforce":[183],"for":[185],"encoder":[188]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-03-18T00:00:00"}
