{"id":"https://openalex.org/W7164862365","doi":"https://doi.org/10.48550/arxiv.2606.14299","title":"What Drives Test-Time Adaptation for CLIP? A Controlled Empirical Study from an Update Perspective","display_name":"What Drives Test-Time Adaptation for CLIP? A Controlled Empirical Study from an Update Perspective","publication_year":2026,"publication_date":"2026-06-12","ids":{"openalex":"https://openalex.org/W7164862365","doi":"https://doi.org/10.48550/arxiv.2606.14299"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.14299","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14299","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.14299","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006022044","display_name":"J W Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Jiazhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138667426","display_name":"Xiao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138647585","display_name":"Zhiming Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhiming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006643010","display_name":"Yaru Sun","orcid":"https://orcid.org/0000-0002-5984-7359"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Yaru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138635473","display_name":"Jingyan Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Jingyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138652848","display_name":"Zhi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhi","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":0.3637000024318695,"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":0.3637000024318695,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.26589998602867126,"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/T13629","display_name":"Text Readability and Simplification","score":0.10599999874830246,"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":0.7081000208854675},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6869999766349792},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.6597999930381775},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.5891000032424927},{"id":"https://openalex.org/keywords/empirical-evidence","display_name":"Empirical evidence","score":0.5120999813079834},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4763999879360199}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7081000208854675},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6869999766349792},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.6597999930381775},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6208000183105469},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.5891000032424927},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.5120999813079834},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4763999879360199},{"id":"https://openalex.org/C2776088982","wikidata":"https://www.wikidata.org/wiki/Q841402","display_name":"Silver bullet","level":2,"score":0.4650999903678894},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.39980000257492065},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.32919999957084656},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.31439998745918274},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C43540301","wikidata":"https://www.wikidata.org/wiki/Q689971","display_name":"Paradigm shift","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26109999418258667}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.14299","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14299","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.14299","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14299","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.6901308298110962,"id":"https://metadata.un.org/sdg/4"}],"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],"(VLMs)":[2],"such":[3],"as":[4,35],"CLIP":[5,34],"have":[6],"become":[7],"a":[8,36,41,80,91,232,242],"standard":[9],"backbone":[10],"for":[11,122,206,244],"open-vocabulary":[12],"recognition,":[13],"yet":[14],"their":[15,65],"zero-shot":[16],"predictions":[17],"remain":[18,73],"vulnerable":[19],"to":[20,33,40,107],"distribution":[21],"shifts":[22,71],"encountered":[23],"at":[24,111],"deployment.":[25],"Test-Time":[26],"Adaptation":[27],"(TTA)":[28],"has":[29,54],"recently":[30],"been":[31],"extended":[32],"lightweight":[37,194],"solution,":[38],"leading":[39],"rapidly":[42],"growing":[43],"body":[44],"of":[45,59,86,95,223,235],"TTA4CLIP":[46,238],"methods.":[47,134],"However,":[48],"empirical":[49,137],"progress":[50],"in":[51,150],"this":[52,76],"area":[53],"largely":[55],"outpaced":[56],"our":[57,227],"understanding":[58,234],"what":[60,108],"truly":[61],"drives":[62],"adaptation,":[63],"where":[64],"gains":[66,156],"originate,":[67],"and":[68,89,128,163,182,193,215,229,240],"under":[69],"which":[70,124],"they":[72],"reliable.":[74],"In":[75],"paper,":[77],"we":[78,145,171,198],"take":[79],"step":[81],"back":[82],"from":[83],"the":[84,147,216,221,236],"pursuit":[85],"state-of-the-art":[87],"accuracy":[88],"conduct":[90],"systematic":[92],"controlled":[93,136],"study":[94,230],"TTA4CLIP.":[96],"We":[97,114,225],"first":[98],"organize":[99],"existing":[100],"methods":[101],"into":[102],"three":[103,141],"unified":[104],"paradigms":[105],"according":[106],"is":[109,202,212],"updated":[110],"test":[112],"time.":[113],"then":[115],"introduce":[116],"TTABC,":[117],"an":[118],"open-source":[119],"TTA":[120],"Benchmark":[121],"CLIP,":[123],"standardizes":[125],"evaluation":[126],"protocols":[127],"integrates":[129],"more":[130],"than":[131,167],"20":[132],"representative":[133],"Our":[135],"analysis":[138],"focuses":[139],"on":[140,220],"key":[142],"areas.":[143],"First,":[144],"determine":[146],"driving":[148],"factors":[149],"parameter-based":[151],"methods,":[152],"revealing":[153],"that":[154,180,200],"adaptation":[155,210],"are":[157],"primarily":[158],"driven":[159],"by":[160],"test-time":[161],"evidence":[162,173,192],"reliable":[164],"proxies":[165],"rather":[166],"heavy":[168,176],"optimization.":[169],"Second,":[170],"explore":[172],"utilization":[174],"beyond":[175],"parameter":[177],"tuning,":[178],"showing":[179],"competitive":[181],"efficient":[183],"performance":[184],"can":[185],"be":[186],"achieved":[187],"through":[188],"cross-":[189],"or":[190],"current-sample":[191],"prototype":[195],"updates.":[196],"Finally,":[197],"demonstrate":[199],"there":[201],"no":[203,208],"silver":[204],"bullet":[205],"TTA:":[207],"single":[209],"paradigm":[211,218],"universally":[213],"optimal,":[214],"preferred":[217],"depends":[219],"nature":[222],"shift.":[224],"hope":[226],"benchmark":[228],"provide":[231],"clearer":[233],"current":[237],"landscape":[239],"establish":[241],"foundation":[243],"further":[245],"research.":[246]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-16T00:00:00"}