{"id":"https://openalex.org/W7133560080","doi":"https://doi.org/10.48550/arxiv.2603.02557","title":"CAPT: Confusion-Aware Prompt Tuning for Reducing Vision-Language Misalignment","display_name":"CAPT: Confusion-Aware Prompt Tuning for Reducing Vision-Language Misalignment","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133560080","doi":"https://doi.org/10.48550/arxiv.2603.02557"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.02557","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02557","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.02557","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102703043","display_name":"M. Shao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shao, Maoyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128085018","display_name":"Yutong Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Yutong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128108930","display_name":"Xinyang Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Xinyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128037793","display_name":"Chuang Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Chuang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068493872","display_name":"Lijuan Sun","orcid":"https://orcid.org/0000-0003-2673-9375"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Lijuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5020360628","display_name":"Guoshun Nan","orcid":"https://orcid.org/0000-0002-1987-2736"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nan, Guoshun","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5102703043"],"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.968999981880188,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.968999981880188,"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":0.012799999676644802,"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":0.002199999988079071,"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/leverage","display_name":"Leverage (statistics)","score":0.7318999767303467},{"id":"https://openalex.org/keywords/confusion","display_name":"Confusion","score":0.6970000267028809},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6158000230789185},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.491100013256073},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4339999854564667},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4113999903202057},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4083000123500824},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3718999922275543}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7318999767303467},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.6970000267028809},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6676999926567078},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6158000230789185},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5216000080108643},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.491100013256073},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4339999854564667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.426800012588501},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4113999903202057},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4083000123500824},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3718999922275543},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.35499998927116394},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.35409998893737793},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3402000069618225},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3368000090122223},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.26080000400543213},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C168065819","wikidata":"https://www.wikidata.org/wiki/Q845566","display_name":"Debugging","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.02557","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02557","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.02557","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02557","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7688872218132019}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-language":[0],"models":[1,62],"like":[2],"CLIP":[3],"have":[4],"achieved":[5],"remarkable":[6],"progress":[7],"in":[8],"cross-modal":[9],"representation":[10],"learning,":[11],"yet":[12],"suffer":[13],"from":[14,65,118],"systematic":[15],"misclassifications":[16],"among":[17],"visually":[18],"and":[19,44,83,104,107,121,132,155,181,186],"semantically":[20],"similar":[21],"categories.":[22],"We":[23],"observe":[24],"that":[25,60,129,170],"such":[26],"confusion":[27,79,100,138],"patterns":[28],"are":[29],"not":[30],"random":[31],"but":[32],"persistently":[33],"occur":[34],"between":[35],"specific":[36],"category":[37],"pairs,":[38],"revealing":[39],"the":[40,119,179],"model's":[41],"intrinsic":[42],"bias":[43],"limited":[45],"fine-grained":[46],"discriminative":[47],"ability.":[48],"To":[49,135],"address":[50],"this,":[51],"we":[52,70,89],"propose":[53],"CAPT,":[54],"a":[55,72,91,108,126,143],"Confusion-Aware":[56],"Prompt":[57],"Tuning":[58],"framework":[59],"enables":[61],"to":[63,75,96,113,151],"learn":[64],"their":[66],"own":[67],"misalignment.":[68],"Specifically,":[69],"construct":[71],"Confusion":[73,93,110],"Bank":[74],"explicitly":[76],"model":[77],"stable":[78],"relationships":[80],"across":[81,140],"categories":[82],"misclassified":[84,116],"samples.":[85],"On":[86],"this":[87],"basis,":[88],"introduce":[90],"Semantic":[92],"Miner":[94,111],"(SEM)":[95],"capture":[97,122],"global":[98,131],"inter-class":[99],"through":[101,125],"semantic":[102],"difference":[103],"commonality":[105],"prompts,":[106],"Sample":[109],"(SAM)":[112],"retrieve":[114],"representative":[115],"instances":[117],"bank":[120],"sample-level":[123,156],"cues":[124],"Diff-Manner":[127],"Adapter":[128],"integrates":[130],"local":[133],"contexts.":[134],"further":[136],"unify":[137],"information":[139],"different":[141],"granularities,":[142],"Multi-Granularity":[144],"Difference":[145],"Expert":[146],"(MGDE)":[147],"module":[148],"is":[149],"designed":[150],"jointly":[152],"leverage":[153],"semantic-":[154],"experts":[157],"for":[158],"more":[159],"robust":[160],"confusion-aware":[161],"reasoning.":[162],"Extensive":[163],"experiments":[164],"on":[165],"11":[166],"benchmark":[167],"datasets":[168],"demonstrate":[169],"our":[171],"method":[172],"significantly":[173],"reduces":[174],"confusion-induced":[175],"errors":[176],"while":[177],"enhancing":[178],"discriminability":[180],"generalization":[182],"of":[183,193],"both":[184],"base":[185],"novel":[187],"classes,":[188],"successfully":[189],"resolving":[190],"50.72":[191],"percent":[192],"confusable":[194],"sample":[195],"pairs.":[196],"Code":[197],"will":[198],"be":[199],"released":[200],"at":[201],"https://github.com/greatest-gourmet/CAPT.":[202]},"counts_by_year":[],"updated_date":"2026-03-05T07:36:02.291473","created_date":"2026-03-05T00:00:00"}