{"id":"https://openalex.org/W7137989366","doi":"https://doi.org/10.1609/aaai.v40i13.38057","title":"DCAC: Dynamic Class-Aware Cache Creates Stronger Out-of-Distribution Detectors","display_name":"DCAC: Dynamic Class-Aware Cache Creates Stronger Out-of-Distribution Detectors","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7137989366","doi":"https://doi.org/10.1609/aaai.v40i13.38057"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i13.38057","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i13.38057","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i13.38057","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123507702","display_name":"Yanqi Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yanqi Wu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129697695","display_name":"Qichao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qichao Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123458700","display_name":"Runhe Lai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Runhe Lai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123494937","display_name":"Xinhua Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinhua Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033396445","display_name":"Jiaxin Zhuang","orcid":"https://orcid.org/0000-0001-9287-4263"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia-Xin Zhuang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129721863","display_name":"Zhilin Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhilin Zhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123389926","display_name":"Weishi Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weishi Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129746801","display_name":"Ruixuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruixuan Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22792023,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"13","first_page":"10817","last_page":"10825"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.45019999146461487,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.45019999146461487,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.1468999981880188,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.12399999797344208,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.6546000242233276},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5900999903678894},{"id":"https://openalex.org/keywords/cache","display_name":"Cache","score":0.5824000239372253},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5015000104904175},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.48739999532699585},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.35030001401901245}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7402999997138977},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.6546000242233276},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5900999903678894},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.5824000239372253},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5015000104904175},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.48739999532699585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38839998841285706},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3474000096321106},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2842000126838684},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C189783530","wikidata":"https://www.wikidata.org/wiki/Q352090","display_name":"CPU cache","level":3,"score":0.26339998841285706},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2531999945640564},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.2531999945640564},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i13.38057","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i13.38057","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/38057","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/38057","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i13.38057","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i13.38057","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1730722784","display_name":null,"funder_award_id":"2023B1515040025","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4183193758","display_name":null,"funder_award_id":"PCL2025AS209","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/F4320321160","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Out-of-distribution":[0],"(OOD)":[1],"detection":[2,120],"remains":[3],"a":[4,24,65,99],"fundamental":[5],"challenge":[6],"for":[7,38,74],"deep":[8],"neural":[9],"networks,":[10],"particularly":[11],"due":[12],"to":[13,44,48,78,103],"overconfident":[14,105],"predictions":[15,86,106],"on":[16,107,135,158],"unseen":[17],"OOD":[18,27,108,119,137,160],"samples":[19,28,81],"during":[20],"testing.":[21],"We":[22],"reveal":[23],"key":[25],"insight:":[26],"predicted":[29,96],"as":[30],"the":[31,49,84],"same":[32],"class,":[33],"or":[34],"given":[35],"high":[36],"probabilities":[37,97],"it,":[39],"are":[40],"visually":[41],"more":[42],"similar":[43],"each":[45,75],"other":[46],"than":[47],"true":[50],"in-distribution":[51],"(ID)":[52],"samples.":[53,89,109],"Motivated":[54],"by":[55,152],"this":[56],"class-specific":[57],"observation,":[58],"we":[59],"propose":[60],"DCAC":[61,90,141],"(Dynamic":[62],"Class-Aware":[63],"Cache),":[64],"training-free,":[66],"test-time":[67],"calibration":[68],"module":[69,102,111],"that":[70,140],"maintains":[71],"separate":[72],"caches":[73],"ID":[76],"class":[77],"collect":[79],"high-entropy":[80],"and":[82,95,125],"calibrate":[83],"raw":[85],"of":[87],"input":[88],"leverages":[91],"cached":[92],"visual":[93],"features":[94],"through":[98],"lightweight":[100],"two-layer":[101],"mitigate":[104],"This":[110],"can":[112],"be":[113],"seamlessly":[114],"integrated":[115,155],"with":[116,156],"various":[117],"existing":[118,144],"methods":[121],"across":[122],"both":[123],"unimodal":[124],"vision-language":[126],"models":[127],"while":[128],"introducing":[129],"minimal":[130],"computational":[131],"overhead.":[132],"Extensive":[133],"experiments":[134],"multiple":[136],"benchmarks":[138],"demonstrate":[139],"significantly":[142],"enhances":[143],"methods,":[145],"achieving":[146],"substantial":[147],"improvements,":[148],"i.e.,":[149],"reducing":[150],"FPR95":[151],"6.55%":[153],"when":[154],"ASH-S":[157],"ImageNet":[159],"benchmark.":[161]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-18T00:00:00"}