{"id":"https://openalex.org/W4415708260","doi":"https://doi.org/10.1109/icme59968.2025.11210236","title":"Adaptive Weighted Parameter Fusion with CLIP for Class-Incremental Learning","display_name":"Adaptive Weighted Parameter Fusion with CLIP for Class-Incremental Learning","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415708260","doi":"https://doi.org/10.1109/icme59968.2025.11210236"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11210236","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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":null,"display_name":"Juncen Guo","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":"Juncen Guo","raw_affiliation_strings":["Fudan University,Academy for Engineering & Technology"],"affiliations":[{"raw_affiliation_string":"Fudan University,Academy for Engineering & Technology","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100685782","display_name":"Xiaoguang Zhu","orcid":"https://orcid.org/0000-0001-9554-2133"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoguang Zhu","raw_affiliation_strings":["University of California,Davis"],"affiliations":[{"raw_affiliation_string":"University of California,Davis","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109819235","display_name":"Liangyu Teng","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":false,"raw_author_name":"Liangyu Teng","raw_affiliation_strings":["Fudan University,Academy for Engineering & Technology"],"affiliations":[{"raw_affiliation_string":"Fudan University,Academy for Engineering & Technology","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090606999","display_name":"Hao Yang","orcid":"https://orcid.org/0009-0001-3231-5475"},"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":"Hao Yang","raw_affiliation_strings":["Fudan University,Academy for Engineering & Technology"],"affiliations":[{"raw_affiliation_string":"Fudan University,Academy for Engineering & Technology","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374987","display_name":"Jing Liu","orcid":"https://orcid.org/0000-0002-2819-0200"},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jing Liu","raw_affiliation_strings":["The University of British Columbia"],"affiliations":[{"raw_affiliation_string":"The University of British Columbia","institution_ids":["https://openalex.org/I141945490"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100356047","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-9846-7359"},"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":"Yang Liu","raw_affiliation_strings":["Fudan University,Academy for Engineering & Technology"],"affiliations":[{"raw_affiliation_string":"Fudan University,Academy for Engineering & Technology","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077588879","display_name":"Liang Song","orcid":"https://orcid.org/0000-0001-5068-4859"},"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":"Liang Song","raw_affiliation_strings":["Fudan University,Academy for Engineering & Technology"],"affiliations":[{"raw_affiliation_string":"Fudan University,Academy for Engineering & Technology","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16141579,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9474999904632568,"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":0.9474999904632568,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.008500000461935997,"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":0.006500000134110451,"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/classifier","display_name":"Classifier (UML)","score":0.54339998960495},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.48969998955726624},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4668999910354614},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4147999882698059},{"id":"https://openalex.org/keywords/incremental-learning","display_name":"Incremental learning","score":0.39629998803138733},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.326200008392334}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7372000217437744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5569000244140625},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.54339998960495},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.48969998955726624},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4668999910354614},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4323999881744385},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41920000314712524},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4090000092983246},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.39629998803138733},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.31779998540878296},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C168031717","wikidata":"https://www.wikidata.org/wiki/Q1530280","display_name":"Balance (ability)","level":2,"score":0.28110000491142273}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11210236","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1621286049","https://openalex.org/W2015861736","https://openalex.org/W2473930607","https://openalex.org/W2602516395","https://openalex.org/W2884282566","https://openalex.org/W2948734064","https://openalex.org/W2963072899","https://openalex.org/W2982220706","https://openalex.org/W3030364939","https://openalex.org/W3034856281","https://openalex.org/W3107810305","https://openalex.org/W3168149265","https://openalex.org/W3180392831","https://openalex.org/W3181434681","https://openalex.org/W4221161784","https://openalex.org/W4225484930","https://openalex.org/W4312238419","https://openalex.org/W4312351187","https://openalex.org/W4312731352","https://openalex.org/W4385486148","https://openalex.org/W4386076680","https://openalex.org/W4387968140","https://openalex.org/W4391614403","https://openalex.org/W4402092710","https://openalex.org/W4402753978","https://openalex.org/W4402981332","https://openalex.org/W4402982173","https://openalex.org/W4403886967"],"related_works":[],"abstract_inverted_index":{"Class-incremental":[0],"Learning":[1],"(CIL)":[2],"enables":[3],"the":[4,24,30,74,87,103,106,116,120,124,137,153,156],"model":[5,25],"to":[6,39,69,77,123],"incrementally":[7],"absorb":[8],"knowledge":[9,31,52],"from":[10],"new":[11,28,55],"classes":[12,34],"and":[13,53,141],"build":[14],"a":[15,47,70,131],"generic":[16],"classifier":[17],"across":[18],"all":[19,115],"previously":[20],"encountered":[21],"classes.":[22,80],"When":[23],"optimizes":[26],"with":[27,92],"classes,":[29],"of":[32,105,109,119,143,155],"previous":[33],"is":[35],"inevitably":[36],"erased,":[37],"leading":[38],"catastrophic":[40],"forgetting.":[41],"Addressing":[42],"this":[43,58,83],"challenge":[44],"requires":[45,62],"making":[46],"trade-off":[48],"between":[49,79],"retaining":[50],"old":[51],"accommodating":[54],"information.":[56],"However,":[57],"balancing":[59],"process":[60],"often":[61],"sacrificing":[63],"some":[64],"information,":[65],"which":[66,97],"can":[67,135],"lead":[68],"partial":[71],"loss":[72],"in":[73],"model\u2019s":[75],"ability":[76],"discriminate":[78],"To":[81],"tackle":[82],"issue,":[84],"we":[85,129],"design":[86],"adaptive":[88],"weighted":[89],"parameter":[90,121],"fusion":[91],"Contrastive":[93],"Language-Image":[94],"Pre-training":[95],"(CLIP),":[96],"not":[98],"only":[99],"takes":[100],"into":[101],"account":[102],"variability":[104],"data":[107,138],"distribution":[108,139],"different":[110],"tasks,":[111],"but":[112],"also":[113],"retains":[114],"effective":[117],"information":[118],"matrix":[122],"greatest":[125],"extent.":[126],"In":[127],"addition,":[128],"introduce":[130],"balance":[132,136],"factor":[133],"that":[134],"alignment":[140],"distinguishability":[142],"adjacent":[144],"tasks.":[145],"Experimental":[146],"results":[147],"on":[148],"several":[149],"traditional":[150],"benchmarks":[151],"validate":[152],"superiority":[154],"proposed":[157],"method.":[158]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-30T00:00:00"}