{"id":"https://openalex.org/W4414170698","doi":"https://doi.org/10.1109/iwqos65803.2025.11143267","title":"EMMU: Efficient Information-Level Multimodal Machine Unlearning with High Model Fidelity","display_name":"EMMU: Efficient Information-Level Multimodal Machine Unlearning with High Model Fidelity","publication_year":2025,"publication_date":"2025-07-02","ids":{"openalex":"https://openalex.org/W4414170698","doi":"https://doi.org/10.1109/iwqos65803.2025.11143267"},"language":"en","primary_location":{"id":"doi:10.1109/iwqos65803.2025.11143267","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos65803.2025.11143267","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACM 33rd International Symposium on Quality of Service (IWQoS)","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/A5100604078","display_name":"Jie Zhang","orcid":"https://orcid.org/0000-0002-0530-3954"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Zhang","raw_affiliation_strings":["School of Computer Science and Technology, University of Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033759584","display_name":"Jiahui Hou","orcid":"https://orcid.org/0000-0002-3340-8585"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahui Hou","raw_affiliation_strings":["School of Computer Science and Technology, University of Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082050264","display_name":"Tie Xiao","orcid":"https://orcid.org/0009-0007-5630-8759"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tie Xiao","raw_affiliation_strings":["School of Computer Science and Technology, University of Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048422436","display_name":"Y. P. Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunyi Huang","raw_affiliation_strings":["School of Computer Science and Technology, University of Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100341798","display_name":"Xiang-Yang Li","orcid":"https://orcid.org/0000-0002-3713-1405"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang-Yang Li","raw_affiliation_strings":["School of Computer Science and Technology, University of Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20089207,"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":"10"},"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":0.9959999918937683,"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":0.9959999918937683,"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":0.9940000176429749,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9805999994277954,"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/fidelity","display_name":"Fidelity","score":0.8179000020027161},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7031000256538391},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5110999941825867},{"id":"https://openalex.org/keywords/abstract-machine","display_name":"Abstract machine","score":0.4275999963283539},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.3865000009536743},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3449000120162964}],"concepts":[{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.8179000020027161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7573999762535095},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7031000256538391},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5960000157356262},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5853999853134155},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5110999941825867},{"id":"https://openalex.org/C88977235","wikidata":"https://www.wikidata.org/wiki/Q787114","display_name":"Abstract machine","level":2,"score":0.4275999963283539},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C2988416141","wikidata":"https://www.wikidata.org/wiki/Q6031139","display_name":"Information loss","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C52622258","wikidata":"https://www.wikidata.org/wiki/Q131222","display_name":"Information theory","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwqos65803.2025.11143267","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos65803.2025.11143267","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACM 33rd International Symposium on Quality of Service (IWQoS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1773149199","https://openalex.org/W1861492603","https://openalex.org/W1895577753","https://openalex.org/W1933349210","https://openalex.org/W1956340063","https://openalex.org/W2101105183","https://openalex.org/W2307512708","https://openalex.org/W2487898712","https://openalex.org/W3120043490","https://openalex.org/W3135378441","https://openalex.org/W3152884768","https://openalex.org/W3154155772","https://openalex.org/W3210059226","https://openalex.org/W4205460703","https://openalex.org/W4221158899","https://openalex.org/W4283796010","https://openalex.org/W4312595359","https://openalex.org/W4382142077","https://openalex.org/W4385245566","https://openalex.org/W4385570172","https://openalex.org/W4385572399","https://openalex.org/W4387796537","https://openalex.org/W4387838615","https://openalex.org/W4389519496","https://openalex.org/W4389524330","https://openalex.org/W4393399871","https://openalex.org/W4402716477","https://openalex.org/W4402726948","https://openalex.org/W4404354786","https://openalex.org/W4404439856","https://openalex.org/W4404575065","https://openalex.org/W4404782753","https://openalex.org/W4406489046"],"related_works":[],"abstract_inverted_index":{"To":[0],"comply":[1],"with":[2,124,136],"the":[3,35,87,125,137,165,188],"\u201cright":[4],"to":[5,20,43,53,69,102,113,172],"be":[6,54],"forgotten,\u201d":[7],"recent":[8],"research":[9],"has":[10,30,193],"introduced":[11],"machine":[12,17,28,61,98],"unlearning":[13,29,99],"techniques":[14],"that":[15,50,120],"enable":[16],"learning":[18],"models":[19,204],"remove":[21],"specific":[22,71,126],"data":[23,48,74],"samples.":[24],"However,":[25],"existing":[26,173],"multimodal":[27,97],"concerns":[31],"about":[32],"efficiency,":[33],"and":[34,115,140,158,167,197,205],"model":[36,88,104,118],"fidelity":[37,105,168],"may":[38],"deteriorate":[39],"after":[40],"unlearning,":[41,62,67],"leading":[42],"meaningless":[44],"outputs":[45],"if":[46],"given":[47],"samples":[49,75],"are":[51,121],"requested":[52],"forgotten.":[55],"Instead":[56],"of":[57,73,169],"focusing":[58],"on":[59,65,150],"datalevel":[60],"we":[63,93],"focus":[64],"information-level":[66],"aiming":[68],"forget":[70],"information":[72,79,127,139],"(such":[76],"as":[77,154],"sensitive":[78,138],"involving":[80],"name":[81],"or":[82],"medical":[83],"condition)":[84],"while":[85],"maintaining":[86],"fidelity.":[89],"In":[90],"this":[91],"work,":[92],"design":[94],"an":[95,178],"efficient":[96],"(EMMU)":[100],"framework":[101],"address":[103],"in":[106],"vision-language":[107,151],"systems.":[108],"The":[109],"core":[110],"idea":[111],"is":[112],"locate":[114],"only":[116],"modify":[117],"parameters":[119,134,143],"highly":[122],"correlated":[123],"(which":[128],"requires":[129],"forgetting).":[130],"EMMU":[131],"locates":[132],"crucial":[133],"associated":[135],"updates":[141],"these":[142],"using":[144,161],"a":[145],"multiobjective":[146],"optimization":[147],"strategy.":[148],"Evaluations":[149],"tasks,":[152],"such":[153],"Visual":[155],"Question":[156],"Answering":[157],"Image":[159],"Captioning,":[160],"multiple":[162,203],"datasets,":[163],"demonstrate":[164],"efficiency":[166,183],"EMMU.":[170],"Compared":[171],"methods,":[174],"our":[175],"method":[176],"obtains":[177],"average":[179,189,196],"of$111":[180],"\\times$improvement,":[181],"boosting":[182],"up":[184,199],"to$1445":[185],"\\times$.":[186],"Meanwhile,":[187],"utility-forget":[190],"balance":[191],"score":[192],"improved$9":[194],"\\times$on":[195],"reached":[198],"to$70":[200],"\\times$,":[201],"across":[202],"datasets.":[206]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
