{"id":"https://openalex.org/W7167592246","doi":"https://doi.org/10.48550/arxiv.2607.05019","title":"Beyond Modality Fusion: Deep Ensembles for Multimodal Classification","display_name":"Beyond Modality Fusion: Deep Ensembles for Multimodal Classification","publication_year":2026,"publication_date":"2026-07-06","ids":{"openalex":"https://openalex.org/W7167592246","doi":"https://doi.org/10.48550/arxiv.2607.05019"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.05019","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.05019","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2607.05019","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049197996","display_name":"Ilya Burenko","orcid":"https://orcid.org/0000-0002-4248-1266"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Burenko, Ilya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140156826","display_name":"Dmitry Vetrov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vetrov, Dmitry","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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.25929999351501465,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.25929999351501465,"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/T10028","display_name":"Topic Modeling","score":0.1185000017285347,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.09709999710321426,"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/modality","display_name":"Modality (human\u2013computer interaction)","score":0.699400007724762},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.48159998655319214},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4546000063419342},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4153999984264374},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4075999855995178},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.4066999852657318},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3750999867916107}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.711899995803833},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.699400007724762},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6654000282287598},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5148000121116638},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.48159998655319214},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4546000063419342},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4153999984264374},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.4066999852657318},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3750999867916107},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.36480000615119934},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.05019","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.05019","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.05019","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.05019","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,38],"multimodal":[1,44,104,180,186],"classification,":[2],"late-fusion":[3,36,67,80],"approaches":[4],"classify":[5],"concatenated":[6],"modality-specific":[7],"features":[8],"extracted":[9],"by":[10,211],"unimodal":[11,59,102,143],"neural":[12],"networks.":[13,37,60],"When":[14],"modality":[15,53,85,120,131,150,163],"imbalance":[16,132],"is":[17],"pronounced,":[18],"various":[19],"regularization":[20],"techniques":[21,93],"have":[22],"been":[23],"proposed":[24],"to":[25,66,83,215],"balance":[26],"the":[27,32,115,137,148,154,161,176,193,220],"learning":[28],"process":[29],"and":[30,73,96,103,108,133,197,207],"overcome":[31],"inferior":[33],"performance":[34,222],"of":[35,58,117,142,178,195,223],"contrast,":[39],"this":[40],"work":[41],"demonstrates":[42],"that":[43,75,100,140,188],"data":[45],"can":[46],"be":[47],"effectively":[48],"classified":[49],"without":[50],"any":[51],"explicit":[52],"fusion,":[54],"using":[55],"deep":[56,64],"ensembles":[57,65,76,141],"We":[61,106],"systematically":[62,174],"compare":[63],"networks":[68],"at":[69],"equal":[70],"parameter":[71],"count":[72],"show":[74],"consistently":[77],"outperform":[78],"state-of-the-art":[79],"methods":[81,99],"designed":[82],"address":[84],"imbalance.":[86],"This":[87],"advantage":[88],"also":[89],"holds":[90],"over":[91,97,191],"intermediate-fusion":[92],"we":[94,182,218],"evaluated":[95],"hybrid":[98],"combine":[101],"predictions.":[105],"propose":[107,183],"empirically":[109],"validate":[110],"a":[111,184],"method":[112],"for":[113],"selecting":[114],"number":[116,194],"models":[118,144,159],"per":[119],"in":[121],"an":[122],"ensemble,":[123],"avoiding":[124],"computationally":[125],"expensive":[126],"exhaustive":[127],"search.":[128],"Under":[129],"extreme":[130],"small":[134],"ensemble":[135,155],"sizes,":[136],"heuristic":[138],"indicates":[139],"trained":[145],"solely":[146],"on":[147],"stronger":[149],"are":[151,203],"preferable;":[152],"as":[153],"scales":[156],"up,":[157],"incorporating":[158],"from":[160],"weaker":[162],"becomes":[164],"beneficial.":[165],"Both":[166],"predictions":[167],"align":[168],"with":[169],"our":[170,201],"empirical":[171],"findings.":[172],"To":[173],"explore":[175],"challenges":[177],"optimizing":[179],"models,":[181],"synthetic":[185,206],"framework":[187],"allows":[189],"control":[190],"both":[192],"modalities":[196],"their":[198],"predictive":[199],"strength;":[200],"findings":[202],"consistent":[204],"across":[205],"real-world":[208],"datasets.":[209],"Finally,":[210],"fitting":[212],"scaling":[213],"laws":[214],"bimodal":[216],"datasets,":[217],"estimate":[219],"asymptotic":[221],"ensembles.":[224]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-08T00:00:00"}