{"id":"https://openalex.org/W4411635536","doi":"https://doi.org/10.1145/3731715.3733300","title":"DASPL: Enhancing Few-Shot Learning with Dual Adapters and a Single-Step Pseudo-Label Cycle","display_name":"DASPL: Enhancing Few-Shot Learning with Dual Adapters and a Single-Step Pseudo-Label Cycle","publication_year":2025,"publication_date":"2025-06-25","ids":{"openalex":"https://openalex.org/W4411635536","doi":"https://doi.org/10.1145/3731715.3733300"},"language":"en","primary_location":{"id":"doi:10.1145/3731715.3733300","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3731715.3733300","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 International Conference on Multimedia Retrieval","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/A5025820196","display_name":"Yanbo Zhang","orcid":"https://orcid.org/0009-0009-2139-6319"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanbo Zhang","raw_affiliation_strings":["Yanshan University, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuhao Liu","orcid":"https://orcid.org/0009-0002-3368-6582"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhao Liu","raw_affiliation_strings":["Yanshan University, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhaoyang Liu","orcid":"https://orcid.org/0009-0001-5867-5073"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoyang Liu","raw_affiliation_strings":["Yanshan University, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100603790","display_name":"Huiying Li","orcid":"https://orcid.org/0000-0002-8123-7768"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huiying Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ruilin Chai","orcid":"https://orcid.org/0009-0008-6000-9644"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruilin Chai","raw_affiliation_strings":["Yanshan University, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080706260","display_name":"Guanghua Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanghua Gu","raw_affiliation_strings":["Yanshan University, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5025820196"],"corresponding_institution_ids":["https://openalex.org/I39333907"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07094431,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1795","last_page":"1803"},"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":0.9991000294685364,"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":0.9991000294685364,"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":0.9797999858856201,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9404000043869019,"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/single-shot","display_name":"Single shot","score":0.7621266841888428},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.702485203742981},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.69849693775177},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.5854400396347046},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.4799383282661438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3890334665775299},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.11563661694526672},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.11522269248962402},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10789093375205994}],"concepts":[{"id":"https://openalex.org/C3019835501","wikidata":"https://www.wikidata.org/wiki/Q1310130","display_name":"Single shot","level":2,"score":0.7621266841888428},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.702485203742981},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.69849693775177},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.5854400396347046},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.4799383282661438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3890334665775299},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.11563661694526672},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11522269248962402},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10789093375205994},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3731715.3733300","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3731715.3733300","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5586466468","display_name":null,"funder_award_id":"62072394","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W12634471","https://openalex.org/W1977295328","https://openalex.org/W2017814585","https://openalex.org/W2047643928","https://openalex.org/W2084342166","https://openalex.org/W2108598243","https://openalex.org/W2138011018","https://openalex.org/W2533598788","https://openalex.org/W2964194231","https://openalex.org/W2979805229","https://openalex.org/W3037492894","https://openalex.org/W3145450063","https://openalex.org/W3159481202","https://openalex.org/W3177096435","https://openalex.org/W3198377975","https://openalex.org/W4292779060","https://openalex.org/W4312629998","https://openalex.org/W4313175608","https://openalex.org/W4382458283","https://openalex.org/W4386066792","https://openalex.org/W4386071547","https://openalex.org/W4386075985","https://openalex.org/W4390873389","https://openalex.org/W4390874269","https://openalex.org/W4402727780","https://openalex.org/W6600731917","https://openalex.org/W6714454088","https://openalex.org/W6778883912","https://openalex.org/W6834781518"],"related_works":["https://openalex.org/W3142396426","https://openalex.org/W2471333042","https://openalex.org/W2497720472","https://openalex.org/W4396643691","https://openalex.org/W4404739899","https://openalex.org/W4409177797","https://openalex.org/W146529714","https://openalex.org/W4402383816","https://openalex.org/W2316500695","https://openalex.org/W1999226266"],"abstract_inverted_index":{"Recently,":[0],"fine-tuning":[1],"vision-language":[2],"models":[3],"like":[4],"CLIP":[5,71],"has":[6],"led":[7],"to":[8,128,160],"impressive":[9],"performance":[10,168],"on":[11,28],"downstream":[12,191],"tasks,":[13],"with":[14,121,169],"approaches":[15],"such":[16],"as":[17],"prompt":[18],"tuning":[19],"and":[20,58,72,95,105,148,193,204],"adapter-based":[21],"methods.":[22],"However,":[23],"these":[24],"methods":[25],"primarily":[26],"rely":[27],"limited":[29],"labeled":[30],"data,":[31],"while":[32],"the":[33,67,85,111,122,137,155,199],"potential":[34],"of":[35,70,174,201],"unlabeled":[36],"data":[37],"remains":[38],"largely":[39],"underexplored.":[40],"In":[41],"this":[42],"paper,":[43],"we":[44,115],"propose":[45],"a":[46,75,79,117,171],"two-stage":[47],"learning":[48,57,87],"framework":[49,64],"inspired":[50],"by":[51],"children's":[52],"cognitive":[53],"development,":[54],"integrating":[55],"supervised":[56,86],"self-evolving":[59,112],"adaptation.":[60,162],"Firstly,":[61],"our":[62,100,164,202],"DASPL":[63,180],"fully":[65],"integrates":[66],"complementary":[68],"strengths":[69],"MoCoV3":[73],"through":[74],"Dual-Adapter":[76],"architecture,":[77],"ensuring":[78,152],"more":[80],"comprehensive":[81],"feature":[82,107],"representation":[83],"in":[84,110],"stage.":[88],"By":[89],"leveraging":[90],"CLIP's":[91],"high-level":[92],"semantic":[93],"alignment":[94],"MoCoV3's":[96],"fine-grained":[97],"visual":[98],"distinctions,":[99],"approach":[101,165,203],"captures":[102],"both":[103],"global":[104],"local":[106],"information.":[108],"Secondly,":[109],"adaptation":[113],"stage,":[114],"employ":[116],"three-branch":[118],"ensemble":[119],"along":[120],"classification":[123,187],"max":[124],"prediction":[125],"margin":[126],"criterion":[127],"refine":[129],"pseudo-label":[130,139],"selection.":[131],"This":[132],"strategy":[133],"not":[134],"only":[135,154],"improves":[136],"overall":[138],"quality":[140],"but":[141],"also":[142],"effectively":[143],"distinguishes":[144],"between":[145],"visually":[146],"similar":[147],"easily":[149],"confused":[150],"categories,":[151],"that":[153,179],"most":[156],"reliable":[157],"samples":[158],"contribute":[159],"model":[161],"Notably,":[163],"achieves":[166],"strong":[167],"just":[170],"single":[172],"iteration":[173],"pseudo-labeling.":[175],"Experimental":[176],"results":[177],"demonstrate":[178],"consistently":[181],"outperforms":[182],"existing":[183],"methods,":[184],"achieving":[185],"higher":[186],"accuracy":[188],"across":[189],"multiple":[190],"tasks":[192],"out-of-distribution":[194],"benchmarks.":[195],"These":[196],"findings":[197],"highlight":[198],"effectiveness":[200],"provide":[205],"new":[206],"insights":[207],"for":[208],"few-shot":[209],"learning.":[210]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
