{"id":"https://openalex.org/W7139964725","doi":"https://doi.org/10.48550/arxiv.2603.18598","title":"Complementary Text-Guided Attention for Zero-Shot Adversarial Robustness","display_name":"Complementary Text-Guided Attention for Zero-Shot Adversarial Robustness","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7139964725","doi":"https://doi.org/10.48550/arxiv.2603.18598"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.18598","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18598","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.2603.18598","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130230353","display_name":"Lu Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130252855","display_name":"Haiyang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Haiyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130217934","display_name":"Changsheng Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Changsheng","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.921999990940094,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.921999990940094,"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":0.016499999910593033,"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":0.012299999594688416,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8539000153541565},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7825000286102295},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.7014999985694885},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.41019999980926514},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.3950999975204468},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3668999969959259}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8539000153541565},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7825000286102295},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7487000226974487},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.7014999985694885},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4880000054836273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41179999709129333},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3668999969959259},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2630000114440918}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.18598","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18598","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.2603.18598","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18598","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Due":[0],"to":[1,25,28,81,116,143,197],"the":[2,83,86,95,105,131,179,187,195,206,226],"impressive":[3],"zero-shot":[4,222],"capabilities,":[5],"pre-trained":[6],"vision-language":[7],"models":[8,109],"(e.g.,":[9],"CLIP),":[10],"have":[11,35],"attracted":[12],"widespread":[13],"attention":[14,102,176,184,192],"and":[15,73,89,107,146,182,202,213,217],"adoption":[16],"across":[17,230],"various":[18],"domains.":[19],"Nonetheless,":[20],"CLIP":[21,87],"has":[22],"been":[23],"observed":[24,36],"be":[26],"susceptible":[27],"adversarial":[29,40,92],"examples.":[30,112],"Through":[31],"experimental":[32],"analysis,":[33],"we":[34,51,128,157],"a":[37,53,160,199],"phenomenon":[38],"wherein":[39],"perturbations":[41],"induce":[42],"shifts":[43],"in":[44,150,221],"text-guided":[45,101],"attention.":[46],"Building":[47],"upon":[48],"this":[49,155],"observation,":[50],"propose":[52,159],"simple":[54],"yet":[55],"effective":[56],"strategy:":[57],"Text-Guided":[58,165],"Attention":[59,70,75,97,166],"for":[60],"Zero-Shot":[61],"Robustness":[62],"(TGA-ZSR).":[63],"This":[64,168],"framework":[65],"incorporates":[66],"two":[67,171],"components:":[68],"Local":[69],"Refinement":[71],"Module":[72,99],"Global":[74,96],"Constraint":[76,98],"Module.":[77],"Our":[78],"goal":[79],"is":[80,115],"maintain":[82,117],"generalization":[84],"of":[85,173,205],"model":[88,118,196],"enhance":[90],"its":[91,148],"robustness.":[93,126],"Additionally,":[94],"acquires":[100],"from":[103],"both":[104],"target":[106],"original":[108],"using":[110],"clean":[111,121],"Its":[113],"objective":[114],"performance":[119,145],"on":[120,135],"samples":[122],"while":[123],"enhancing":[124],"overall":[125],"However,":[127],"observe":[129],"that":[130,211],"method":[132,169],"occasionally":[133],"focuses":[134],"irrelevant":[136],"or":[137],"spurious":[138],"features,":[139],"which":[140],"can":[141],"lead":[142],"suboptimal":[144],"undermine":[147],"robustness":[149],"certain":[151],"scenarios.":[152],"To":[153],"overcome":[154],"limitation,":[156],"further":[158],"novel":[161],"approach":[162],"called":[163],"Complementary":[164],"(Comp-TGA).":[167],"integrates":[170],"types":[172],"foreground":[174],"attention:":[175],"guided":[177],"by":[178,186],"class":[180],"prompt":[181],"reversed":[183],"driven":[185],"non-class":[188],"prompt.":[189],"These":[190],"complementary":[191],"mechanisms":[193],"allow":[194],"capture":[198],"more":[200],"comprehensive":[201],"accurate":[203],"representation":[204],"foreground.":[207],"The":[208],"experiments":[209],"validate":[210],"TGA-ZSR":[212],"Comp-TGA":[214],"yield":[215],"9.58%":[216],"11.95%":[218],"improvements":[219],"respectively,":[220],"robust":[223],"accuracy":[224],"over":[225],"current":[227],"state-of-the-art":[228],"techniques":[229],"16":[231],"datasets.":[232]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-21T00:00:00"}
