{"id":"https://openalex.org/W7164866797","doi":"https://doi.org/10.1145/3805622.3810771","title":"Evidential Uncertainty Modulated Adaptive Predictive Contrastive Learning for Multimodal Fusion","display_name":"Evidential Uncertainty Modulated Adaptive Predictive Contrastive Learning for Multimodal Fusion","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164866797","doi":"https://doi.org/10.1145/3805622.3810771"},"language":null,"primary_location":{"id":"doi:10.1145/3805622.3810771","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810771","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3805622.3810771","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079216677","display_name":"Qiuyu Mei","orcid":"https://orcid.org/0000-0001-8090-6600"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiuyu Mei","raw_affiliation_strings":["Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0001-8090-6600","affiliations":[{"raw_affiliation_string":"Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003746498","display_name":"Hong Yu","orcid":"https://orcid.org/0000-0003-0667-8413"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Yu","raw_affiliation_strings":["Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0003-0667-8413","affiliations":[{"raw_affiliation_string":"Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100563659","display_name":"Shijie Yu","orcid":"https://orcid.org/0009-0008-0038-2148"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shijie Yu","raw_affiliation_strings":["Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0009-0000-9872-7467","affiliations":[{"raw_affiliation_string":"Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004787718","display_name":"Yan Yang","orcid":"https://orcid.org/0000-0001-8648-9692"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Yang","raw_affiliation_strings":["Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0001-8648-9692","affiliations":[{"raw_affiliation_string":"Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.95150493,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"487","last_page":"496"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.267300009727478,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.267300009727478,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2409999966621399,"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/T10028","display_name":"Topic Modeling","score":0.0982000008225441,"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/discriminative-model","display_name":"Discriminative model","score":0.5809000134468079},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.5605999827384949},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4884999990463257},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4555000066757202},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.40549999475479126},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.39980000257492065},{"id":"https://openalex.org/keywords/evidential-reasoning-approach","display_name":"Evidential reasoning approach","score":0.37139999866485596}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6980000138282776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6682000160217285},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5827999711036682},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5809000134468079},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.5605999827384949},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4884999990463257},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4555000066757202},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.40549999475479126},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C156201811","wikidata":"https://www.wikidata.org/wiki/Q5418360","display_name":"Evidential reasoning approach","level":4,"score":0.37139999866485596},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.3458999991416931},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3208000063896179},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C28901747","wikidata":"https://www.wikidata.org/wiki/Q177571","display_name":"Decision theory","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.26489999890327454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3805622.3810771","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810771","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3805622.3810771","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810771","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7648827433586121,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2963383024","https://openalex.org/W2964010806","https://openalex.org/W2964051877","https://openalex.org/W3005971801","https://openalex.org/W3095024162","https://openalex.org/W3174525637","https://openalex.org/W3216235189","https://openalex.org/W4224926219","https://openalex.org/W4225650823","https://openalex.org/W4234587807","https://openalex.org/W4313136445","https://openalex.org/W4372341093","https://openalex.org/W4376226279","https://openalex.org/W4379806284","https://openalex.org/W4382318593","https://openalex.org/W4385571742","https://openalex.org/W4385801378","https://openalex.org/W4386071847","https://openalex.org/W4390873312","https://openalex.org/W4392131573","https://openalex.org/W4396576599","https://openalex.org/W4402716394","https://openalex.org/W4402981520","https://openalex.org/W4406552230","https://openalex.org/W4407677402","https://openalex.org/W7160023821","https://openalex.org/W7160326358"],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"learning":[1,89],"has":[2],"achieved":[3],"remarkable":[4],"success":[5],"by":[6],"integrating":[7],"heterogeneous":[8],"information":[9],"from":[10],"multiple":[11,186],"sources.":[12],"However,":[13],"most":[14],"existing":[15],"methods":[16],"either":[17],"treat":[18],"all":[19],"samples":[20,177],"uniformly":[21],"or":[22],"rely":[23],"on":[24,80,185],"deterministic":[25],"prediction":[26],"correctness":[27],"to":[28,47,75,90,121,130],"guide":[29],"cross-modal":[30,77],"alignment,":[31],"overlooking":[32],"the":[33,172],"varying":[34],"degrees":[35],"of":[36,174],"epistemic":[37,94],"uncertainty":[38,71],"inherent":[39],"in":[40],"each":[41],"modality.":[42],"Such":[43],"assumptions":[44],"often":[45],"lead":[46],"noise":[48],"propagation,":[49],"especially":[50],"when":[51],"models":[52],"exhibit":[53],"over-confident":[54],"yet":[55],"unreliable":[56,176],"predictions.":[57],"To":[58],"address":[59],"this":[60],"limitation,":[61],"we":[62,84,110],"propose":[63],"Adaptive":[64],"Predictive":[65],"Contrastive":[66],"Learning":[67],"(AdaPCL),":[68],"an":[69],"evidential":[70,87,105],"modulated":[72],"framework":[73],"designed":[74],"regulate":[76],"interactions":[78],"based":[79],"modality-specific":[81,93],"reliability.":[82,106],"Specifically,":[83],"first":[85],"employ":[86],"deep":[88],"explicitly":[91],"quantify":[92],"uncertainty,":[95],"establishing":[96],"a":[97,112,132,155],"dual-view":[98],"assessment":[99],"that":[100,116,169,190],"calibrates":[101],"discriminative":[102],"confidence":[103],"with":[104],"Building":[107],"upon":[108],"this,":[109],"introduce":[111],"continuous":[113],"reliability-modulated":[114],"mechanism":[115],"synthesizes":[117],"these":[118],"dual":[119],"perspectives":[120],"assign":[122],"soft,":[123],"instance-specific":[124],"weights.":[125],"This":[126],"design":[127],"enables":[128],"AdaPCL":[129,191],"formulate":[131],"unified":[133],"and":[134,164],"adaptive":[135],"contrastive":[136],"objective":[137],"encompassing":[138],"three":[139],"complementary":[140],"alignment":[141],"strategies:":[142],"(i)":[143],"Symmetric":[144],"Alignment":[145],"for":[146,160],"mutually":[147,175],"reliable":[148,156],"modality":[149,157],"pairs,":[150],"(ii)":[151],"Directional":[152],"Distillation":[153],"where":[154],"provides":[158],"pseudo-supervision":[159],"its":[161],"uncertain":[162],"counterpart,":[163],"(iii)":[165],"Reliability-Aware":[166],"Slack":[167],"Regularization":[168],"adaptively":[170],"attenuates":[171],"influence":[173],"without":[178],"enforcing":[179],"rigid":[180],"geometric":[181],"constraints.":[182],"Extensive":[183],"experiments":[184],"benchmark":[187],"datasets":[188],"demonstrate":[189],"consistently":[192],"outperforms":[193],"baseline":[194],"multimodal":[195],"classification":[196],"methods.":[197],"Code":[198],"is":[199],"available":[200],"at":[201],"https://github.com/yuhongcqupt/AdaPCL.":[202]},"counts_by_year":[],"updated_date":"2026-06-16T07:37:23.134862","created_date":"2026-06-16T00:00:00"}
