{"id":"https://openalex.org/W7123543798","doi":"https://doi.org/10.1109/mmsp64401.2025.11324153","title":"DBAB: A Dual-Branch Adaptive Balance Framework with Optimized Plasticity Branch for Class-Incremental Learning","display_name":"DBAB: A Dual-Branch Adaptive Balance Framework with Optimized Plasticity Branch for Class-Incremental Learning","publication_year":2025,"publication_date":"2025-09-21","ids":{"openalex":"https://openalex.org/W7123543798","doi":"https://doi.org/10.1109/mmsp64401.2025.11324153"},"language":null,"primary_location":{"id":"doi:10.1109/mmsp64401.2025.11324153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp64401.2025.11324153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Workshop on Multimedia Signal Processing (MMSP)","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/A5100443906","display_name":"Xinyu Chen","orcid":"https://orcid.org/0009-0000-0671-4942"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinyu Chen","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066434602","display_name":"Heqian Qiu","orcid":"https://orcid.org/0000-0002-0963-0311"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heqian Qiu","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122969957","display_name":"Chenghao Qi","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenghao Qi","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122960348","display_name":"Ruisong Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruisong Dai","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122931242","display_name":"Hongliang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongliang Li","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100443906"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.85769869,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"210","last_page":"215"},"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":0.9027000069618225,"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":0.9027000069618225,"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":0.019899999722838402,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.006200000178068876,"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/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.7771999835968018},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6437000036239624},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5378999710083008},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.49889999628067017},{"id":"https://openalex.org/keywords/plasticity","display_name":"Plasticity","score":0.49619999527931213},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4675999879837036},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.3977000117301941},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.3700000047683716}],"concepts":[{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.7771999835968018},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.660099983215332},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6437000036239624},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5378999710083008},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49889999628067017},{"id":"https://openalex.org/C79186407","wikidata":"https://www.wikidata.org/wiki/Q472074","display_name":"Plasticity","level":2,"score":0.49619999527931213},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4927999973297119},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4675999879837036},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3377000093460083},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.2786000072956085},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mmsp64401.2025.11324153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp64401.2025.11324153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2015563892","https://openalex.org/W2060277733","https://openalex.org/W3157898411","https://openalex.org/W3175294706","https://openalex.org/W3180392831","https://openalex.org/W3198659451","https://openalex.org/W4225484930","https://openalex.org/W4312238419","https://openalex.org/W4312351187","https://openalex.org/W4312651322","https://openalex.org/W4382464382","https://openalex.org/W4386076680","https://openalex.org/W4390871664","https://openalex.org/W4390871722","https://openalex.org/W4394597752","https://openalex.org/W4400680576","https://openalex.org/W4402092710"],"related_works":[],"abstract_inverted_index":{"In":[0],"the":[1,6,17,119,144,157,163,167,178,205,215],"context":[2],"of":[3,21,75,89,180,186,208],"class-incremental":[4],"learning,":[5],"primary":[7],"challenge":[8],"for":[9,48,100,118,140,193],"models":[10,24,38,47],"is":[11,51,175],"to":[12,39,42,59,72,126,201,204],"overcome":[13],"catastrophic":[14,32],"forgetting.":[15],"Leveraging":[16],"strong":[18],"generalization":[19,63,105,155],"ability":[20],"frozen":[22,108],"pre-trained":[23,46,96,109],"can":[25],"significantly":[26],"enhance":[27],"model":[28,70,97],"performance":[29,64,239],"and":[30,62,98,103,135,142,154,211,213,235],"alleviate":[31],"forgetting":[33],"during":[34,156],"training.":[35],"To":[36,161],"enable":[37],"better":[40,150],"adapt":[41,203],"downstream":[43,101,187],"tasks,":[44,102,188,210],"fine-tuning":[45,68],"new":[49,209],"tasks":[50],"a":[52,81,90,95,104,137,149,198],"common":[53],"approach.":[54],"However,":[55],"current":[56],"works":[57],"struggle":[58],"balance":[60,116,124,145,151],"plasticity":[61,91,153,164],"after":[65],"fine-tuning,":[66],"as":[67,233],"causes":[69],"parameters":[71],"overwrite":[73],"knowledge":[74],"old":[76],"tasks.":[77],"This":[78,147],"paper":[79],"proposes":[80],"Dual-Branch":[82],"Adaptive":[83,171],"Balance":[84],"(DBAB)":[85],"framework,":[86,169],"which":[87],"consists":[88],"branch":[92,106,165],"fine-tuned":[93],"from":[94,132],"optimized":[99],"with":[107],"parameters.":[110],"The":[111],"framework":[112],"designs":[113],"an":[114,170],"adaptive":[115],"mechanism":[117],"dual":[120],"branches,":[121,134],"introduces":[122],"learnable":[123],"coefficients":[125],"dynamically":[127,202],"fuse":[128],"class":[129],"prototype":[130],"distances":[131],"both":[133],"devises":[136],"loss":[138],"function":[139],"training":[141],"regularizing":[143],"coefficients.":[146],"ensures":[148],"between":[152],"incremental":[158],"learning":[159,185],"process.":[160],"optimize":[162],"in":[166,183],"DBAB":[168,223],"Plasticity":[172],"Module":[173],"(APM)":[174],"proposed.":[176],"Considering":[177],"heterogeneity":[179],"embedding":[181],"distributions":[182,207],"continuous":[184],"APM":[189],"employs":[190],"Mahalanobis":[191,216],"distance":[192],"anisotropic":[194],"feature":[195],"alignment,":[196],"uses":[197],"covariance":[199],"matrix":[200],"heterogeneous":[206],"improves":[212],"stabilizes":[214],"distance-based":[217],"classification":[218],"method.Experimental":[219],"results":[220],"show":[221],"that":[222],"outperforms":[224],"multiple":[225],"state-of-the-art":[226],"(SOTA)":[227],"methods":[228],"on":[229],"benchmark":[230],"datasets":[231],"such":[232],"CIFAR100":[234],"CUB200,":[236],"demonstrating":[237],"significant":[238],"improvements.":[240]},"counts_by_year":[],"updated_date":"2026-01-14T23:44:37.837170","created_date":"2026-01-14T00:00:00"}
