{"id":"https://openalex.org/W4402353846","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650069","title":"Prompt-Based Memory Bank for Continual Test-Time Domain Adaptation in Vision-Language Models","display_name":"Prompt-Based Memory Bank for Continual Test-Time Domain Adaptation in Vision-Language Models","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402353846","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650069"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101985101","display_name":"Ran Wang","orcid":"https://orcid.org/0009-0004-8397-3410"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Ran Wang","raw_affiliation_strings":["University of Technology Sydney,Australian Artificial Intelligence Institute FEIT,Sydney,NSW,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Technology Sydney,Australian Artificial Intelligence Institute FEIT,Sydney,NSW,Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071218354","display_name":"Hua Zuo","orcid":"https://orcid.org/0000-0002-9122-0775"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hua Zuo","raw_affiliation_strings":["University of Technology Sydney,Australian Artificial Intelligence Institute FEIT,Sydney,NSW,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Technology Sydney,Australian Artificial Intelligence Institute FEIT,Sydney,NSW,Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087852871","display_name":"Zhen Fang","orcid":"https://orcid.org/0000-0003-0602-6255"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Zhen Fang","raw_affiliation_strings":["University of Technology Sydney,Australian Artificial Intelligence Institute FEIT,Sydney,NSW,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Technology Sydney,Australian Artificial Intelligence Institute FEIT,Sydney,NSW,Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100675577","display_name":"Jie L\u00fc","orcid":"https://orcid.org/0000-0003-0690-4732"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jie Lu","raw_affiliation_strings":["University of Technology Sydney,Australian Artificial Intelligence Institute FEIT,Sydney,NSW,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Technology Sydney,Australian Artificial Intelligence Institute FEIT,Sydney,NSW,Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101985101"],"corresponding_institution_ids":["https://openalex.org/I114017466"],"apc_list":null,"apc_paid":null,"fwci":0.4604,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6318627,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9986000061035156,"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/computer-science","display_name":"Computer science","score":0.7447038292884827},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.605030357837677},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5969603061676025},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.5254834294319153},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4800882935523987},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.47321146726608276},{"id":"https://openalex.org/keywords/memory-test","display_name":"Memory test","score":0.45978549122810364},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36164236068725586},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.12532776594161987},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.07063496112823486}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7447038292884827},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.605030357837677},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5969603061676025},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.5254834294319153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4800882935523987},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.47321146726608276},{"id":"https://openalex.org/C3017990537","wikidata":"https://www.wikidata.org/wiki/Q6815759","display_name":"Memory test","level":3,"score":0.45978549122810364},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36164236068725586},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.12532776594161987},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.07063496112823486},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2970476646","https://openalex.org/W3025988480","https://openalex.org/W3119543788","https://openalex.org/W3164101295","https://openalex.org/W3185341429","https://openalex.org/W3198377975","https://openalex.org/W3205071530","https://openalex.org/W3206798603","https://openalex.org/W3210129272","https://openalex.org/W4284961860","https://openalex.org/W4285345750","https://openalex.org/W4286897344","https://openalex.org/W4296151208","https://openalex.org/W4297738480","https://openalex.org/W4306175908","https://openalex.org/W4312232016","https://openalex.org/W4312310776","https://openalex.org/W4312897837","https://openalex.org/W4312935996","https://openalex.org/W4320858551","https://openalex.org/W4385245566","https://openalex.org/W4386065353","https://openalex.org/W4386065721","https://openalex.org/W4389009058","https://openalex.org/W6687400500","https://openalex.org/W6687483927","https://openalex.org/W6739901393","https://openalex.org/W6752049347","https://openalex.org/W6755207826","https://openalex.org/W6757555829","https://openalex.org/W6784333009","https://openalex.org/W6788125050","https://openalex.org/W6790019176","https://openalex.org/W6791353385","https://openalex.org/W6800895557","https://openalex.org/W6802517928","https://openalex.org/W6803227292","https://openalex.org/W6811433417","https://openalex.org/W6839701648","https://openalex.org/W6842574522","https://openalex.org/W6846159801","https://openalex.org/W6850116420","https://openalex.org/W6850568775"],"related_works":["https://openalex.org/W4394775207","https://openalex.org/W4389474468","https://openalex.org/W4300172004","https://openalex.org/W4321649381","https://openalex.org/W2997645659","https://openalex.org/W3180787869","https://openalex.org/W3203792196","https://openalex.org/W2955455867","https://openalex.org/W4295929828","https://openalex.org/W3156096827"],"abstract_inverted_index":{"In":[0,184],"dynamic":[1],"environments,":[2,89,188],"the":[3,18,35,107,151,210,216,225],"generalization":[4,213],"capabilities":[5,214],"of":[6,21,215],"large-scale":[7,121],"vision":[8,122],"language":[9,123],"models":[10,50,124],"tend":[11],"to":[12,17,27,46,51,91,126],"decline.":[13],"This":[14],"is":[15],"attributed":[16],"evolving":[19],"distribution":[20],"target":[22,54],"domains":[23,55],"over":[24],"time,":[25],"leading":[26,90],"misalignment":[28],"between":[29,200],"image":[30,153],"and":[31,71,76,84,94,154,160,174,193,195,204,212,240],"text":[32,155],"pairings,":[33],"affecting":[34],"model\u2019s":[36],"performance.":[37],"Addressing":[38],"this,":[39],"Test-Time":[40,100,145],"Adaptation":[41,101,146],"(TTA)":[42],"has":[43],"been":[44],"proposed":[45],"adapt":[47],"pre-trained":[48],"source":[49],"these":[52,133],"changing":[53,69,187],"during":[56],"testing":[57],"phases.":[58],"However,":[59],"traditional":[60],"TTA":[61],"approaches,":[62],"which":[63],"are":[64,79,117],"designed":[65],"for":[66,120,143,236],"a":[67,137,170,197],"single":[68],"scenario":[70],"mainly":[72],"depend":[73],"on":[74,106,158],"self-training":[75],"entropy":[77],"minimization,":[78],"easily":[80],"affected":[81],"by":[82],"extreme":[83],"novel":[85,138],"samples":[86],"in":[87,234],"long-term":[88,113,185],"error":[92],"accumulation":[93],"catastrophic":[95],"forgetting.":[96],"Although":[97],"previous":[98],"Continual":[99,144],"(Continual":[102],"TTA)":[103],"methods":[104],"based":[105],"teacher-student":[108],"framework":[109],"can":[110],"effectively":[111,180],"address":[112],"adaptation":[114,239,243],"issues,":[115],"they":[116],"not":[118],"feasible":[119],"due":[125],"their":[127],"high":[128,175,191],"memory":[129,141,166],"requirements.":[130],"To":[131],"overcome":[132],"challenges,":[134],"we":[135],"introduce":[136],"approach:":[139],"Prompt-based":[140],"bank":[142],"(PCoTTA).":[147],"PCoTTA":[148,179,189,223],"uniquely":[149],"freezes":[150],"CLIP":[152,217],"encoders,":[156],"focusing":[157],"updating":[159],"storing":[161],"trainable":[162],"prompts,":[163],"significantly":[164,208],"reducing":[165],"usage.":[167],"By":[168],"implementing":[169],"stable":[171],"pseudo-label":[172],"strategy":[173],"gradient":[176],"sensitivity":[177],"updating,":[178],"learns":[181],"new":[182,202],"knowledge.":[183],"dynamically":[186],"demonstrates":[190],"stability":[192],"accuracy":[194,235],"achieves":[196],"good":[198],"balance":[199],"learning":[201],"information":[203],"retaining":[205],"existing":[206],"knowledge,":[207],"enhancing":[209],"adaptability":[211],"model.":[218],"Through":[219],"extensive":[220],"experimental":[221],"comparisons,":[222],"surpasses":[224],"current":[226],"state-of-the-art":[227],"methods,":[228],"achieving":[229],"an":[230],"average":[231],"2%":[232],"improvement":[233],"both":[237],"test-time":[238,242],"continual":[241],"tasks.":[244]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-07T13:39:58.223016","created_date":"2025-10-10T00:00:00"}
