{"id":"https://openalex.org/W7167062782","doi":"https://doi.org/10.48550/arxiv.2607.00423","title":"Selective Test-Time Debiasing for CLIP via Reward Gating","display_name":"Selective Test-Time Debiasing for CLIP via Reward Gating","publication_year":2026,"publication_date":"2026-07-01","ids":{"openalex":"https://openalex.org/W7167062782","doi":"https://doi.org/10.48550/arxiv.2607.00423"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.00423","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00423","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":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.2607.00423","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122921798","display_name":"Jaeho Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Jaeho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000765561","display_name":"Ji-soo Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jisoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139906788","display_name":"Hyeondong Woo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Woo, Hyeondong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019980342","display_name":"Mingyu Jeon","orcid":"https://orcid.org/0000-0002-8285-5197"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeon, Mingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058873046","display_name":"Sunjae Yoon","orcid":"https://orcid.org/0000-0001-7458-5273"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoon, Sunjae","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139942010","display_name":"Junyeong Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Junyeong","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.6535000205039978,"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":0.6535000205039978,"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":0.09470000118017197,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.042899999767541885,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/debiasing","display_name":"Debiasing","score":0.9983000159263611},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.3824999928474426},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.3587999939918518},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.3407999873161316},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.3043000102043152}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.9983000159263611},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7354000210762024},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3619999885559082},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.27320000529289246},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2644999921321869}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.00423","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00423","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":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.2607.00423","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00423","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.43642160296440125,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision":[0],"language":[1],"models":[2],"(VLMs)":[3],"demonstrate":[4,130],"strong":[5],"zero-shot":[6,137],"performance,":[7],"but":[8],"often":[9],"perpetuate":[10],"social":[11],"stereotypes":[12,56],"in":[13,47,57],"person-centric":[14],"queries,":[15,49],"yielding":[16],"skewed":[17],"demographic":[18],"distributions.":[19],"Current":[20],"debiasing":[21,42,52,91],"methods":[22],"apply":[23],"uniform":[24,143],"bias":[25,34,104,132],"corrections":[26],"across":[27],"all":[28],"input":[29,94,108],"queries":[30,70],"regardless":[31],"of":[32,106,142],"their":[33],"sensitivity,":[35],"creating":[36],"a":[37,82],"fundamental":[38],"fairness--utility":[39],"trade-off.":[40],"Strong":[41],"distorts":[43],"semantically":[44],"meaningful":[45],"information":[46],"bias-insensitive":[48,69,121],"while":[50,113,134],"weak":[51],"fails":[53],"to":[54],"mitigate":[55],"bias-sensitive":[58,74],"ones.":[59],"This":[60],"one-size-fits-all":[61],"approach":[62],"hampers":[63],"simultaneously":[64,135],"achieving":[65],"high":[66],"utility":[67],"on":[68,73,93,102,116,124],"and":[71],"fairness":[72,99,125],"queries.":[75],"We":[76],"introduce":[77],"Reward-Gated":[78],"Test-Time":[79],"Adaptation":[80],"(RG-TTA),":[81],"reinforcement":[83],"learning-based":[84],"test-time":[85,110],"adaptation":[86],"framework":[87],"that":[88],"selectively":[89],"applies":[90],"based":[92,101],"sensitivity.":[95],"RG-TTA":[96],"adaptively":[97],"triggers":[98],"regularization":[100],"the":[103,140],"sensitivity":[105],"each":[107],"during":[109],"policy":[111],"adaptation,":[112],"focusing":[114],"exclusively":[115],"optimizing":[117],"cross-modal":[118],"alignment":[119],"for":[120],"inputs.":[122],"Experiments":[123],"benchmarks":[126],"(e.g.,":[127],"FairFace,":[128],"UTKFace)":[129],"substantial":[131],"reduction":[133],"improving":[136],"utility,":[138],"resolving":[139],"trade-off":[141],"debiasing.":[144]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-03T00:00:00"}