{"id":"https://openalex.org/W7160433042","doi":"https://doi.org/10.48550/arxiv.2605.03820","title":"Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration","display_name":"Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160433042","doi":"https://doi.org/10.48550/arxiv.2605.03820"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.03820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03820","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"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.2605.03820","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101786662","display_name":"Xun Jiang","orcid":"https://orcid.org/0000-0003-2209-651X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Xun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135430198","display_name":"Yufan Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Yufan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047334751","display_name":"Disen Hu","orcid":"https://orcid.org/0009-0007-6251-5375"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Disen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101152067","display_name":"Yuqing Hou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hou, Yuqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135434044","display_name":"Yazhou Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Yazhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135448098","display_name":"Fumin Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Fumin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135516756","display_name":"Heng Tao Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Heng Tao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135503960","display_name":"Xing Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Xing","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":0.40720000863075256,"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":0.40720000863075256,"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":0.20579999685287476,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.07349999994039536,"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/benchmark","display_name":"Benchmark (surveying)","score":0.6704000234603882},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5967000126838684},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5162000060081482},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.47440001368522644},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4672999978065491},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4546000063419342},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.43470001220703125},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.40529999136924744},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.37310001254081726},{"id":"https://openalex.org/keywords/conformal-map","display_name":"Conformal map","score":0.3652999997138977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7214000225067139},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6704000234603882},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6298999786376953},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5967000126838684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5867999792098999},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5162000060081482},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.47440001368522644},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4672999978065491},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4546000063419342},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.43470001220703125},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.40529999136924744},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C98214594","wikidata":"https://www.wikidata.org/wiki/Q850275","display_name":"Conformal map","level":2,"score":0.3652999997138977},{"id":"https://openalex.org/C84945661","wikidata":"https://www.wikidata.org/wiki/Q7366567","display_name":"Root cause","level":2,"score":0.358599990606308},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.3319000005722046},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29670000076293945},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.29249998927116394},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.03820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03820","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.03820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03820","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.8092801570892334,"display_name":"Peace, Justice and strong institutions"}],"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],"often":[2,25],"grapples":[3],"with":[4,72],"the":[5,38,42,70,73,111,129,140,154,159,165],"challenge":[6],"of":[7,44,82],"low-quality":[8],"data,":[9],"which":[10,64,102,127],"predominantly":[11],"manifests":[12],"as":[13],"two":[14,96],"facets:":[15],"modality":[16],"imbalance":[17],"and":[18,47,108,177],"noisy":[19,178],"corruption.":[20],"While":[21],"these":[22],"issues":[23],"are":[24],"studied":[26],"in":[27,37,87],"isolation,":[28],"we":[29,54,147],"argue":[30],"that":[31,93,181],"they":[32],"share":[33],"a":[34,56,88,117,150],"common":[35],"root":[36],"predictive":[39],"uncertainty":[40],"towards":[41,142],"reliability":[43,137],"individual":[45],"modalities":[46],"instances":[48],"during":[49,132],"learning.":[50],"In":[51],"this":[52],"paper,":[53],"propose":[55],"unified":[57],"framework,":[58],"termed":[59],"Conformal":[60],"Predictive":[61],"Self-Calibration":[62],"(CPSC),":[63],"leverages":[65],"conformal":[66,118,155],"prediction":[67],"to":[68,75,120,157],"equip":[69],"model":[71],"ability":[74],"perform":[76],"self-guided":[77],"calibration":[78],"on-the-fly.":[79],"The":[80],"core":[81],"our":[83,182],"proposed":[84],"CPSC":[85,183],"lies":[86],"novel":[89],"self-calibrating":[90],"training":[91,166],"loop":[92],"seamlessly":[94],"integrates":[95],"key":[97],"modules:":[98],"(1)":[99],"Representation":[100],"Self-Calibration,":[101,126],"decomposes":[103],"unimodal":[104],"features":[105],"into":[106],"components,":[107],"selectively":[109],"fuses":[110],"most":[112],"robust":[113],"ones":[114],"identified":[115],"by":[116],"predictor":[119,156],"enhance":[121],"feature":[122],"resilience.":[123],"(2)":[124],"Gradient":[125],"recalibrates":[128],"gradient":[130],"flow":[131],"backpropagation":[133],"based":[134],"on":[135,170],"instance-wise":[136],"scores,":[138],"steering":[139],"optimization":[141],"more":[143],"trustworthy":[144],"directions.":[145],"Furthermore,":[146],"also":[148],"devise":[149],"self-update":[151],"strategy":[152],"for":[153],"ensure":[158],"entire":[160],"system":[161],"co-evolves":[162],"consistently":[163,185],"throughout":[164],"process.":[167],"Extensive":[168],"experiments":[169],"six":[171],"benchmark":[172],"datasets":[173],"under":[174],"both":[175],"imbalanced":[176],"settings":[179],"demonstrate":[180],"framework":[184],"outperforms":[186],"existing":[187],"state-of-the-art":[188],"methods.":[189],"Our":[190],"code":[191],"is":[192],"available":[193],"at":[194],"https://github.com/XunCHN/CPSC.":[195]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-07T00:00:00"}
