{"id":"https://openalex.org/W4415536843","doi":"https://doi.org/10.1145/3746027.3755519","title":"Boosting Multi-Modal Alignment: Geometric Feature Separation for Class Incremental Learning","display_name":"Boosting Multi-Modal Alignment: Geometric Feature Separation for Class Incremental Learning","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415536843","doi":"https://doi.org/10.1145/3746027.3755519"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755519","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755519","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","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/A5025755839","display_name":"Guoqiang Liang","orcid":"https://orcid.org/0000-0002-8710-5520"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoqiang Liang","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0002-8710-5520","affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chuan Qin","orcid":"https://orcid.org/0009-0006-0471-2728"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Qin","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0009-0006-0471-2728","affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042411622","display_name":"De Cheng","orcid":"https://orcid.org/0000-0003-1932-4390"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"De Cheng","raw_affiliation_strings":["Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0003-1932-4390","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101626020","display_name":"Shizhou Zhang","orcid":"https://orcid.org/0000-0002-5914-7109"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shizhou Zhang","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0002-5914-7109","affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028235866","display_name":"Yanning Zhang","orcid":"https://orcid.org/0000-0002-2977-8057"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanning Zhang","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an City, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0002-2977-8057","affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an City, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1880","last_page":"1889"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9914000034332275,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.989300012588501,"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/boosting","display_name":"Boosting (machine learning)","score":0.5468000173568726},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4823000133037567},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4765999913215637},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.44290000200271606},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43220001459121704},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.382999986410141},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.3741999864578247},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3677000105381012},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3407999873161316}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7450000047683716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6905999779701233},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5468000173568726},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4823000133037567},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4765999913215637},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.44290000200271606},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43220001459121704},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.382999986410141},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3479999899864197},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2815999984741211},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.2800000011920929},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.27410000562667847},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.26420000195503235},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755519","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755519","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5474364752","display_name":null,"funder_award_id":"2022JC-DW-08","funder_id":"https://openalex.org/F4320336567","funder_display_name":"Natural Science Basic Research Program of Shaanxi Province"},{"id":"https://openalex.org/G8710510137","display_name":null,"funder_award_id":"62376218?62176198","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336567","display_name":"Natural Science Basic Research Program of Shaanxi Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W12634471","https://openalex.org/W2108598243","https://openalex.org/W2964189064","https://openalex.org/W3065974826","https://openalex.org/W3180392831","https://openalex.org/W4205991051","https://openalex.org/W4292794834","https://openalex.org/W4312238419","https://openalex.org/W4361741631","https://openalex.org/W4379929801","https://openalex.org/W4386065833","https://openalex.org/W4386075557","https://openalex.org/W4386076527","https://openalex.org/W4386076680","https://openalex.org/W4387757724","https://openalex.org/W4389524317","https://openalex.org/W4390872020","https://openalex.org/W4390872344","https://openalex.org/W4390872975","https://openalex.org/W4391202802","https://openalex.org/W4392173735","https://openalex.org/W4402702936","https://openalex.org/W4402727193","https://openalex.org/W4402753699","https://openalex.org/W4402754307","https://openalex.org/W4402774761","https://openalex.org/W4407361604","https://openalex.org/W4409364132"],"related_works":[],"abstract_inverted_index":{"Class":[0],"Incremental":[1],"Learning":[2],"(CIL)":[3],"aims":[4],"to":[5,27,53,64,84,171],"continually":[6],"learn":[7],"new":[8],"classes":[9],"from":[10,128,189],"a":[11,93,116,123,129],"stream":[12],"of":[13,45,100,141,181,191,220],"data":[14,69],"without":[15],"forgetting":[16],"previously":[17],"learned":[18],"ones.":[19],"Recent":[20],"approaches":[21],"have":[22],"leveraged":[23],"pre-trained":[24],"models":[25,38],"(PTMs)":[26],"improve":[28],"performance,":[29,212],"especially":[30],"vision-language":[31],"models,":[32],"which":[33,57],"offer":[34],"better":[35,150],"generalization":[36],"than":[37],"trained":[39],"solely":[40],"on":[41,49,198],"visual":[42,102,137,158],"data.":[43],"Many":[44],"these":[46,73],"methods":[47],"rely":[48],"simple":[50],"language":[51],"templates":[52],"generate":[54],"class":[55,143,184,221],"representations,":[56],"then":[58,169],"serve":[59],"as":[60],"classifiers.":[61],"However,":[62],"due":[63],"differences":[65],"between":[66],"the":[67,97,136,142,177,218],"pre-training":[68],"and":[70,103,134,138,185],"downstream":[71],"tasks,":[72],"textual":[74,104,139],"features":[75,105,140],"can":[76],"become":[77],"too":[78],"similar":[79],"for":[80,120,160],"certain":[81],"classes,":[82],"leading":[83],"prediction":[85],"errors.":[86],"To":[87,149],"address":[88],"this":[89,147],"issue,":[90],"we":[91,114,154],"propose":[92],"method":[94,209],"that":[95,207],"optimizes":[96],"geometric":[98],"structure":[99],"both":[101,135],"across":[106],"different":[107],"classes.":[108,193],"Inspired":[109],"by":[110],"neural":[111],"collapse":[112],"theory,":[113],"introduce":[115],"multi-modal":[117],"alignment":[118],"strategy:":[119],"each":[121,161],"class,":[122],"reference":[124],"vector":[125],"is":[126,168,226],"chosen":[127],"simplex":[130],"Equiangular":[131],"Tight":[132],"Frame,":[133],"are":[144],"aligned":[145],"with":[146],"vector.":[148],"capture":[151],"intra-class":[152],"variations,":[153],"also":[155],"construct":[156],"multiple":[157],"prototypes":[159,190],"class.":[162],"A":[163],"multi-prototype":[164],"supervised":[165],"contrastive":[166],"loss":[167],"employed":[170],"pull":[172],"an":[173],"image":[174],"feature":[175],"toward":[176],"closest":[178],"matching":[179],"prototype":[180],"its":[182,214],"true":[183],"push":[186],"it":[187],"away":[188],"other":[192],"We":[194],"evaluate":[195],"our":[196,208],"approach":[197],"five":[199],"widely":[200],"used":[201],"CIL":[202],"benchmarks.":[203],"The":[204],"results":[205],"show":[206],"achieves":[210],"state-of-the-art":[211],"demonstrating":[213],"effectiveness":[215],"in":[216],"addressing":[217],"challenges":[219],"incremental":[222],"learning.":[223],"Our":[224],"code":[225],"available":[227],"at":[228],"https://github.com/qcNPU/NCSCMP.":[229]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-25T00:00:00"}