{"id":"https://openalex.org/W4408355604","doi":"https://doi.org/10.1109/icassp49660.2025.10888693","title":"When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective","display_name":"When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408355604","doi":"https://doi.org/10.1109/icassp49660.2025.10888693"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10888693","doi":"https://doi.org/10.1109/icassp49660.2025.10888693","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10888693","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":"1520-6149","issn":["1520-6149","2379-190X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2409.01821","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104182909","display_name":"Hsi-Ai Tsao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Hsi-Ai Tsao","raw_affiliation_strings":["National Tsing Hua University,Hsinchu,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Tsing Hua University,Hsinchu,Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076380995","display_name":"Lei Hsiung","orcid":"https://orcid.org/0000-0001-7065-2336","observed_orcids":["https://orcid.org/0000-0001-7065-2336"]},"institutions":[{"id":"https://openalex.org/I107672454","display_name":"Dartmouth College","ror":"https://ror.org/049s0rh22","country_code":"US","type":"education","lineage":["https://openalex.org/I107672454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lei Hsiung","raw_affiliation_strings":["Dartmouth College,NH,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dartmouth College,NH,USA","institution_ids":["https://openalex.org/I107672454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050344371","display_name":"Pin\u2010Yu Chen","orcid":"https://orcid.org/0000-0003-1039-8369","observed_orcids":["https://orcid.org/0000-0003-1039-8369","https://orcid.org/0009-0000-1440-7870","https://orcid.org/0009-0007-6751-1090","https://orcid.org/0009-0008-1182-583X"]},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pin-Yu Chen","raw_affiliation_strings":["IBM Research,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research,NY,USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062800747","display_name":"Tsung-Yi Ho","orcid":"https://orcid.org/0000-0001-7348-5625","observed_orcids":["https://orcid.org/0000-0001-7348-5625"]},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Tsung-Yi Ho","raw_affiliation_strings":["The Chinese University of Hong Kong,Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong,Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9416,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66102748,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.7050999999046326,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.7050999999046326,"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":0.2865999937057495,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.005100000184029341,"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/visual-prompting","display_name":"visual prompting","score":0.9959999918937683},{"id":"https://openalex.org/keywords/linear-probing","display_name":"linear probing","score":0.9990000128746033},{"id":"https://openalex.org/keywords/vision-language-models","display_name":"vision-language models","score":0.9900000095367432},{"id":"https://openalex.org/keywords/log-likelihood-ratio","display_name":"log-likelihood ratio","score":0.9589999914169312},{"id":"https://openalex.org/keywords/parameter-efficient-transfer-learning","display_name":"parameter-efficient transfer learning","score":0.9290000200271606},{"id":"https://openalex.org/keywords/out-of-distribution-generalization","display_name":"out-of-distribution generalization","score":0.8560000061988831},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.5099999904632568}],"concepts":[{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.7959305644035339},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6848451495170593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5510990619659424},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4320400059223175}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icassp49660.2025.10888693","doi":"https://doi.org/10.1109/icassp49660.2025.10888693","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10888693","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":"1520-6149","issn":["1520-6149","2379-190X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2409.01821","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.01821","pdf_url":"https://arxiv.org/pdf/2409.01821","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2409.01821","doi":"https://doi.org/10.48550/arxiv.2409.01821","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2409.01821","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2409.01821","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.01821","pdf_url":"https://arxiv.org/pdf/2409.01821","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4408355604.pdf","grobid_xml":"https://content.openalex.org/works/W4408355604.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1574447377","https://openalex.org/W1965555277","https://openalex.org/W1985514943","https://openalex.org/W2078483536","https://openalex.org/W2531327146","https://openalex.org/W2767414122","https://openalex.org/W2810192346","https://openalex.org/W2867167548","https://openalex.org/W2969717149","https://openalex.org/W3007522628","https://openalex.org/W3034643863","https://openalex.org/W3035742266","https://openalex.org/W3166764129","https://openalex.org/W4225747866","https://openalex.org/W4281838252","https://openalex.org/W4297163900","https://openalex.org/W4312347790","https://openalex.org/W4318906579","https://openalex.org/W4382318051","https://openalex.org/W4386075538","https://openalex.org/W4387635758","https://openalex.org/W4389169586","https://openalex.org/W4390871949","https://openalex.org/W4393157525","https://openalex.org/W4408355604"],"related_works":["https://openalex.org/W4392902625","https://openalex.org/W4312091889","https://openalex.org/W4416399821","https://openalex.org/W4402352497","https://openalex.org/W7164822190","https://openalex.org/W4281838252","https://openalex.org/W4387635758","https://openalex.org/W4382603086","https://openalex.org/W4389983960","https://openalex.org/W4407124431"],"abstract_inverted_index":{"Adapting":[0],"pre-trained":[1],"models":[2],"to":[3,52,78,86,94],"new":[4],"tasks":[5],"can":[6,20,39],"exhibit":[7],"varying":[8],"effectiveness":[9],"across":[10],"datasets.":[11],"Visual":[12],"prompting,":[13],"a":[14,34,47,79],"state-of-the-art":[15],"parameter-efficient":[16],"transfer":[17,36],"learning":[18,37],"method,":[19,38],"significantly":[21],"improve":[22],"the":[23,29,42,54,65],"performance":[24],"of":[25,57],"out-of-distribution":[26],"tasks.":[27],"On":[28],"other":[30],"hand,":[31],"linear":[32,61],"probing,":[33],"standard":[35],"sometimes":[40],"become":[41],"best":[43],"approach.":[44],"We":[45],"propose":[46],"log-likelihood":[48],"ratio":[49],"(LLR)":[50],"approach":[51],"analyze":[53],"comparative":[55],"benefits":[56],"visual":[58,70],"prompting":[59],"and":[60],"probing.":[62],"By":[63],"employing":[64],"LLR":[66],"score":[67],"alongside":[68],"resource-efficient":[69],"prompts":[71],"approximations,":[72],"our":[73],"cost-effective":[74],"measure":[75],"attains":[76],"up":[77,93],"100-fold":[80],"reduction":[81],"in":[82],"run":[83],"time":[84],"compared":[85],"full":[87],"training,":[88],"while":[89],"achieving":[90],"prediction":[91],"accuracies":[92],"91%.":[95],"The":[96],"source":[97],"code":[98],"is":[99],"available":[100],"at":[101],"VP-LLR.":[102]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-11-01T11:40:01.658920","created_date":"2025-03-13T00:00:00"}
