{"id":"https://openalex.org/W7161004069","doi":"https://doi.org/10.1016/j.neunet.2026.109094","title":"VPAF-FSCIL: Virtual prototype calibration and parameter-adaptive freezing for few-shot class-incremental learning","display_name":"VPAF-FSCIL: Virtual prototype calibration and parameter-adaptive freezing for few-shot class-incremental learning","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161004069","doi":"https://doi.org/10.1016/j.neunet.2026.109094","pmid":"https://pubmed.ncbi.nlm.nih.gov/42161072"},"language":"en","primary_location":{"id":"doi:10.1016/j.neunet.2026.109094","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neunet.2026.109094","pdf_url":null,"source":{"id":"https://openalex.org/S123019304","display_name":"Neural Networks","issn_l":"0893-6080","issn":["0893-6080","1879-2782"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Networks","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.neunet.2026.109094","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014862300","display_name":"Ye Yao","orcid":"https://orcid.org/0000-0002-7012-9307"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Yao","raw_affiliation_strings":["Fudan University, Shanghai, 200438, China"],"raw_orcid":"https://orcid.org/0009-0003-7781-8150","affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, 200438, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030640908","display_name":"Junxi Li","orcid":"https://orcid.org/0000-0002-9428-9751"},"institutions":[{"id":"https://openalex.org/I47449453","display_name":"Kean University","ror":"https://ror.org/04wzzqn13","country_code":"US","type":"education","lineage":["https://openalex.org/I47449453"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junxi Li","raw_affiliation_strings":["College of Business and Public Management, Kean University, Union, New Jersey, 07083, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Business and Public Management, Kean University, Union, New Jersey, 07083, USA","institution_ids":["https://openalex.org/I47449453"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5136073385","display_name":"Chen Xiong","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiong Chen","raw_affiliation_strings":["Fudan University, Shanghai, 200438, China. Electronic address: chenxiong@fudan.edu.cn"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, 200438, China. Electronic address: chenxiong@fudan.edu.cn","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5136073385"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":{"value":3350,"currency":"USD","value_usd":3350},"apc_paid":{"value":3350,"currency":"USD","value_usd":3350},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87970103,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"203","issue":null,"first_page":"109094","last_page":"109094"},"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":0.6209999918937683,"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":0.6209999918937683,"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/T11448","display_name":"Face recognition and analysis","score":0.05090000107884407,"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/T10057","display_name":"Face and Expression Recognition","score":0.0414000004529953,"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/calibration","display_name":"Calibration","score":0.6983000040054321},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.28380000591278076},{"id":"https://openalex.org/keywords/virtual-reality","display_name":"Virtual reality","score":0.24950000643730164},{"id":"https://openalex.org/keywords/virtual-instrument","display_name":"Virtual instrument","score":0.24819999933242798}],"concepts":[{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.6983000040054321},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6794000267982483},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5922999978065491},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4851999878883362},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3508000075817108},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.34209999442100525},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C199639397","wikidata":"https://www.wikidata.org/wiki/Q1788588","display_name":"Engineering drawing","level":1,"score":0.28360000252723694},{"id":"https://openalex.org/C194969405","wikidata":"https://www.wikidata.org/wiki/Q170519","display_name":"Virtual reality","level":2,"score":0.24950000643730164},{"id":"https://openalex.org/C2989149083","wikidata":"https://www.wikidata.org/wiki/Q7935121","display_name":"Virtual instrument","level":3,"score":0.24819999933242798}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.neunet.2026.109094","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neunet.2026.109094","pdf_url":null,"source":{"id":"https://openalex.org/S123019304","display_name":"Neural Networks","issn_l":"0893-6080","issn":["0893-6080","1879-2782"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Networks","raw_type":"journal-article"},{"id":"pmid:42161072","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42161072","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural networks : the official journal of the International Neural Network Society","raw_type":null}],"best_oa_location":{"id":"doi:10.1016/j.neunet.2026.109094","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neunet.2026.109094","pdf_url":null,"source":{"id":"https://openalex.org/S123019304","display_name":"Neural Networks","issn_l":"0893-6080","issn":["0893-6080","1879-2782"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Networks","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2473930607","https://openalex.org/W2919115771","https://openalex.org/W3065974826","https://openalex.org/W4281261996","https://openalex.org/W4292973282","https://openalex.org/W4388037272","https://openalex.org/W4393029150","https://openalex.org/W4400111616","https://openalex.org/W4409248913"],"related_works":[],"abstract_inverted_index":{"Class":[0,43],"Incremental":[1,44],"Learning":[2,45],"(CIL)":[3],"enables":[4],"models":[5],"to":[6,99,105,140],"learn":[7],"new":[8,26,55,106,145,171],"classes":[9,56,165],"in":[10,32,189],"images":[11],"while":[12,166],"retaining":[13],"knowledge":[14],"of":[15,54,163],"previously":[16],"learned":[17],"classes.":[18,107,146,172],"However,":[19],"conventional":[20],"CIL":[21],"assumes":[22],"abundant":[23],"data":[24],"for":[25,144,170],"classes,":[27],"which":[28,72,159],"is":[29],"often":[30],"unrealistic":[31],"real-world":[33],"applications":[34],"such":[35],"as":[36],"autonomous":[37],"driving":[38],"and":[39,69,103,128,178,193],"medical":[40],"diagnosis.":[41],"Few-Shot":[42],"(FSCIL)":[46],"addresses":[47],"this":[48],"issue":[49],"by":[50,109],"enabling":[51],"the":[52,117,155],"learning":[53],"with":[57,91],"minimal":[58],"data.":[59],"Nevertheless,":[60],"it":[61],"faces":[62],"challenges":[63],"like":[64],"catastrophic":[65],"forgetting,":[66],"feature":[67,101],"drift,":[68],"class":[70,137],"imbalance,":[71],"can":[73],"hinder":[74],"effective":[75],"adaptation.":[76],"To":[77],"tackle":[78],"these":[79],"challenges,":[80],"we":[81,115],"propose":[82],"a":[83,87,92,122,125],"method":[84,184],"that":[85,181],"integrates":[86,134],"feature-decoupled":[88],"network":[89],"architecture":[90],"Parameter-Adaptive":[93],"Freezing":[94],"(PAF)":[95],"training":[96],"strategy,":[97],"aiming":[98],"improve":[100],"stability":[102],"adaptability":[104],"Inspired":[108],"Equiangular":[110],"Tight":[111],"Frame":[112],"(ETF)":[113],"theory,":[114],"decouple":[116],"model":[118],"into":[119],"three":[120],"components:":[121],"Feature":[123,126],"Extractor,":[124],"Distributor,":[127],"an":[129],"ETF":[130],"Classifier.":[131],"This":[132],"structure":[133],"adaptive":[135],"virtual":[136],"space":[138],"allocation":[139],"ensure":[141],"optimal":[142],"embeddings":[143],"Moreover,":[147],"PAF":[148],"selectively":[149],"freezes":[150],"critical":[151],"parameters":[152],"based":[153],"on":[154],"Fisher":[156],"Information":[157],"Matrix,":[158],"helps":[160],"minimize":[161],"forgetting":[162],"old":[164],"simultaneously":[167],"improving":[168],"generalization":[169],"Extensive":[173],"experiments":[174],"across":[175],"CIFAR-100,":[176],"miniImageNet,":[177],"CUB-200-2011":[179],"demonstrate":[180],"our":[182],"proposed":[183],"outperforms":[185],"state-of-the-art":[186],"FSCIL":[187],"methods":[188],"both":[190],"classification":[191],"accuracy":[192],"stability.":[194]},"counts_by_year":[],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2026-05-14T00:00:00"}