{"id":"https://openalex.org/W7128608635","doi":"https://doi.org/10.1109/tpami.2026.3663788","title":"Robust Trusted Conflictive Multiview Collaborative Contrastive Learning","display_name":"Robust Trusted Conflictive Multiview Collaborative Contrastive Learning","publication_year":2026,"publication_date":"2026-02-11","ids":{"openalex":"https://openalex.org/W7128608635","doi":"https://doi.org/10.1109/tpami.2026.3663788","pmid":"https://pubmed.ncbi.nlm.nih.gov/41671120"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2026.3663788","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2026.3663788","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","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/A5112409425","display_name":"Shaobo Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I146620803","display_name":"Wenzhou University","ror":"https://ror.org/020hxh324","country_code":"CN","type":"education","lineage":["https://openalex.org/I146620803"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shaobo Hu","raw_affiliation_strings":["College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China","institution_ids":["https://openalex.org/I146620803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022167669","display_name":"Hui Huang","orcid":"https://orcid.org/0000-0002-9119-954X"},"institutions":[{"id":"https://openalex.org/I146620803","display_name":"Wenzhou University","ror":"https://ror.org/020hxh324","country_code":"CN","type":"education","lineage":["https://openalex.org/I146620803"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Huang","raw_affiliation_strings":["College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9119-954X","affiliations":[{"raw_affiliation_string":"College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China","institution_ids":["https://openalex.org/I146620803"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Nan Zhang","orcid":"https://orcid.org/0000-0001-9620-5665"},"institutions":[{"id":"https://openalex.org/I146620803","display_name":"Wenzhou University","ror":"https://ror.org/020hxh324","country_code":"CN","type":"education","lineage":["https://openalex.org/I146620803"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Zhang","raw_affiliation_strings":["College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9620-5665","affiliations":[{"raw_affiliation_string":"College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China","institution_ids":["https://openalex.org/I146620803"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013783407","display_name":"Shiliang Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiliang Sun","raw_affiliation_strings":["School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-7069-3752","affiliations":[{"raw_affiliation_string":"School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5112409425"],"corresponding_institution_ids":["https://openalex.org/I146620803"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24699991,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"48","issue":"6","first_page":"7161","last_page":"7176"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.3652999997138977,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.3652999997138977,"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":0.28780001401901245,"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":0.04430000111460686,"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/robustness","display_name":"Robustness (evolution)","score":0.6657000184059143},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4893999993801117},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.46709999442100525},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.43059998750686646},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4169999957084656},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4018000066280365},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.396699994802475},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3644999861717224}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8429999947547913},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6815999746322632},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6657000184059143},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5952000021934509},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4893999993801117},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.46709999442100525},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4018000066280365},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.396699994802475},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.36079999804496765},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3422999978065491},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2026.3663788","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2026.3663788","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:41671120","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41671120","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 pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7703110575675964,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G4971749353","display_name":null,"funder_award_id":"62576206","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W3108655343","https://openalex.org/W3111896518","https://openalex.org/W3123445154","https://openalex.org/W4200560153","https://openalex.org/W4224926219","https://openalex.org/W4283800180","https://openalex.org/W4291910533","https://openalex.org/W4382466079","https://openalex.org/W4388676610","https://openalex.org/W4388953073","https://openalex.org/W4390685528","https://openalex.org/W4393156830","https://openalex.org/W4394015595","https://openalex.org/W4394966953","https://openalex.org/W4400324640","https://openalex.org/W4401728098","https://openalex.org/W4403791052","https://openalex.org/W4405632414","https://openalex.org/W4405754868","https://openalex.org/W4406755373","https://openalex.org/W4406857474","https://openalex.org/W4407638569","https://openalex.org/W4407948757","https://openalex.org/W4408254227","https://openalex.org/W4408667555","https://openalex.org/W4408776722","https://openalex.org/W4409247055","https://openalex.org/W4409347344","https://openalex.org/W4409364502","https://openalex.org/W4410949498","https://openalex.org/W4411079415","https://openalex.org/W4411203505","https://openalex.org/W4411550717","https://openalex.org/W7127274961","https://openalex.org/W7143467482"],"related_works":[],"abstract_inverted_index":{"Although":[0],"multiview":[1,22,54,102],"learning":[2,55,123,138,165],"methods":[3,39,56,63],"have":[4,46],"been":[5,47],"widely":[6],"studied,":[7],"they":[8],"mostly":[9],"focus":[10],"on":[11,188],"improving":[12],"accuracy":[13],"while":[14],"ignoring":[15],"decision":[16,59],"uncertainty.":[17],"In":[18,172],"the":[19,35,51,94,126,148,152,193],"real":[20],"world,":[21],"data":[23],"often":[24,64],"encounters":[25],"misalignment":[26],"issues,":[27],"resulting":[28],"in":[29,40,100],"conflictive":[30,74,101],"instances":[31],"and":[32,97,117,142,160,169,175],"further":[33],"limiting":[34],"application":[36],"of":[37,53,128,139,195],"these":[38,129],"safety-critical":[41],"domains.":[42],"Recently,":[43],"some":[44],"efforts":[45],"made":[48],"to":[49,69,72,113,124,155,166,180],"improve":[50],"reliability":[52],"by":[57],"estimating":[58],"uncertainty,":[60],"but":[61],"most":[62],"experience":[65],"performance":[66],"degradation":[67],"due":[68],"their":[70],"inability":[71],"handle":[73],"instances.":[75],"To":[76],"address":[77],"this":[78],"issue,":[79],"we":[80],"propose":[81],"a":[82,182],"Robust":[83],"Trusted":[84],"Conflictive":[85],"Multiview":[86],"Collaborative":[87],"Contrastive":[88],"Learning":[89],"(RCMCL)":[90],"method,":[91],"which":[92],"enhances":[93],"model's":[95],"robustness":[96],"generalization":[98],"ability":[99],"scenarios.":[103],"Specifically,":[104],"RCMCL":[105,135,196],"first":[106,146],"uses":[107],"an":[108],"evidential":[109],"deep":[110],"neural":[111],"network":[112],"construct":[114],"view-specific":[115],"opinions,":[116],"then":[118,161],"employs":[119,162],"dissonance-based":[120],"evidence":[121,141,154,177],"contrastive":[122,164],"enhance":[125],"consistency":[127],"opinions":[130],"across":[131],"different":[132],"views.":[133],"Subsequently,":[134],"performs":[136],"collaborative":[137],"consistent":[140,168,174],"complementary":[143,153,170,176],"evidence.":[144,171],"It":[145],"introduces":[147],"vacuity":[149],"degree":[150],"into":[151],"extract":[156],"more":[157],"useful":[158],"information,":[159],"category-level":[163],"separate":[167],"addition,":[173],"is":[178],"combined":[179],"make":[181],"joint":[183],"decision.":[184],"Finally,":[185],"experimental":[186],"results":[187],"eight":[189],"benchmark":[190],"datasets":[191],"verify":[192],"superiority":[194],"over":[197],"state-of-the-art":[198],"methods.":[199]},"counts_by_year":[],"updated_date":"2026-05-09T06:09:20.037420","created_date":"2026-02-12T00:00:00"}