{"id":"https://openalex.org/W4402706231","doi":"https://doi.org/10.1145/3664647.3681368","title":"Enhancing Adaptive Deep Networks for Image Classification via Uncertainty-aware Decision Fusion","display_name":"Enhancing Adaptive Deep Networks for Image Classification via Uncertainty-aware Decision Fusion","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4402706231","doi":"https://doi.org/10.1145/3664647.3681368"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3681368","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3664647.3681368","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3664647.3681368","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113415719","display_name":"Xu Zhang","orcid":"https://orcid.org/0000-0002-2104-9845"},"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":"Xu Zhang","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100532332","display_name":"Xie Zhipeng","orcid":"https://orcid.org/0000-0003-1340-391X"},"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":"Zhipeng Xie","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080937507","display_name":"Haiyang Yu","orcid":"https://orcid.org/0000-0002-1717-0474"},"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":"Haiyang Yu","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101488348","display_name":"Qitong Wang","orcid":"https://orcid.org/0000-0001-6360-3800"},"institutions":[{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Qitong Wang","raw_affiliation_strings":["Universite Paris Cite, Paris, France"],"affiliations":[{"raw_affiliation_string":"Universite Paris Cite, Paris, France","institution_ids":["https://openalex.org/I204730241"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100396080","display_name":"Peng Wang","orcid":"https://orcid.org/0000-0002-8136-9621"},"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":"Peng Wang","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100392156","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0003-0264-788X"},"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":"Wei Wang","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5113415719"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":1.0425,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.81051712,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"8595","last_page":"8603"},"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":0.9994999766349792,"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":0.9994999766349792,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.996399998664856,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7888106107711792},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7730333209037781},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.706016480922699},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6705663204193115},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6278961896896362},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42800667881965637},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38923293352127075}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7888106107711792},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7730333209037781},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.706016480922699},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6705663204193115},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6278961896896362},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42800667881965637},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38923293352127075}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3664647.3681368","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3664647.3681368","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2408.13744","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.13744","pdf_url":"https://arxiv.org/pdf/2408.13744","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3664647.3681368","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3664647.3681368","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6700000166893005,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1578800471","https://openalex.org/W2056716515","https://openalex.org/W2124868070","https://openalex.org/W2125035899","https://openalex.org/W2152761983","https://openalex.org/W2194775991","https://openalex.org/W2789758093","https://openalex.org/W2811513716","https://openalex.org/W2962858109","https://openalex.org/W2963446712","https://openalex.org/W2963993763","https://openalex.org/W2981812042","https://openalex.org/W3035424951","https://openalex.org/W3094397005","https://openalex.org/W4205319111","https://openalex.org/W4224926219","https://openalex.org/W4283818601","https://openalex.org/W4382317673","https://openalex.org/W4386065876","https://openalex.org/W4390190323"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2055243143","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4230611425","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W3215138031","https://openalex.org/W4380075502"],"abstract_inverted_index":{"Handling":[0],"varying":[1,34,219],"computational":[2],"resources":[3,48],"is":[4,178,228],"a":[5,139,161],"critical":[6],"issue":[7],"in":[8],"modern":[9],"AI":[10],"applications.":[11],"Adaptive":[12],"deep":[13,105,116],"networks,":[14],"featuring":[15],"the":[16,41,46,55,74,86,94,100,121,126,132,155,167,172,183,188,199,231],"dynamic":[17],"employment":[18],"of":[19,103,158,175,201],"multiple":[20,95],"classifier":[21,43,57,70,96,169],"heads":[22,71,97],"among":[23],"different":[24],"layers,":[25],"have":[26],"been":[27],"proposed":[28,179],"to":[29,98,130,153,170,180,214],"address":[30],"classification":[31],"tasks":[32],"under":[33],"computing":[35,220],"resources.":[36],"Existing":[37],"approaches":[38],"typically":[39],"utilize":[40],"last":[42,56,75,168],"supported":[44],"by":[45,150],"available":[47,229],"for":[49,77],"inference,":[50],"as":[51],"they":[52],"believe":[53],"that":[54,68,119,142,165],"always":[58],"performs":[59],"better":[60],"across":[61],"all":[62],"classes.":[63,79],"However,":[64],"our":[65,202],"findings":[66],"indicate":[67],"earlier":[69],"can":[72],"outperform":[73],"head":[76],"certain":[78],"Based":[80],"on":[81,114,205,222],"this":[82],"observation,":[83],"we":[84],"introduce":[85],"Collaborative":[87,190],"Decision":[88,191],"Making":[89,192],"(CDM)":[90],"module,":[91],"which":[92],"fuses":[93],"enhance":[99,182],"inference":[101],"performance":[102],"adaptive":[104,224],"networks.":[106,225],"CDM":[107,184,209],"incorporates":[108],"an":[109],"uncertainty-aware":[110],"fusion":[111,144,156],"method":[112],"based":[113],"evidential":[115],"learning":[117,173],"(EDL),":[118],"utilizes":[120],"reliability":[122],"(uncertainty":[123],"values)":[124],"from":[125],"first":[127],"c-1":[128],"classifiers":[129,177],"improve":[131,154],"c-th":[133],"classifier'":[134],"accuracy.":[135],"We":[136],"also":[137],"design":[138],"balance":[140],"term":[141],"reduces":[143],"saturation":[145],"and":[146,210],"unfairness":[147],"issues":[148],"caused":[149],"EDL":[151],"constraints":[152],"quality":[157],"CDM.":[159],"Finally,":[160],"regularized":[162],"training":[163],"strategy":[164],"uses":[166],"guide":[171],"process":[174],"early":[176],"further":[181],"module's":[185],"effect,":[186],"called":[187],"Guided":[189],"(GCDM)":[193],"framework.":[194],"The":[195,226],"experimental":[196],"evaluation":[197],"demonstrates":[198],"effectiveness":[200],"approaches.":[203],"Results":[204],"ImageNet":[206],"datasets":[207],"show":[208],"GCDM":[211],"obtain":[212],"0.4%":[213],"2.8%":[215],"accuracy":[216],"improvement":[217],"(under":[218],"resources)":[221],"popular":[223],"code":[227],"at":[230],"link":[232],"https://github.com/Meteor-Stars/GCDM_AdaptiveNet.":[233]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
