{"id":"https://openalex.org/W4416251538","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228001","title":"A Unified Multi-Class Anomaly Detection Framework Based on Vision Foundation Models","display_name":"A Unified Multi-Class Anomaly Detection Framework Based on Vision Foundation Models","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251538","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228001"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228001","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228001","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5077066510","display_name":"Yue Zhuo","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Zhuo","raw_affiliation_strings":["Xi&#x2019;an Jiaotong University,School of Computer Science and Technology,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an Jiaotong University,School of Computer Science and Technology,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050052141","display_name":"Wei Xi","orcid":"https://orcid.org/0000-0001-9348-2982"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Xi","raw_affiliation_strings":["Xi&#x2019;an Jiaotong University,School of Computer Science and Technology,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an Jiaotong University,School of Computer Science and Technology,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I87445476"],"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":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.989799976348877,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.989799976348877,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.0013000000035390258,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.0013000000035390258,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.8817999958992004},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.49129998683929443},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.45489999651908875},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.3824999928474426},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.3792000114917755},{"id":"https://openalex.org/keywords/completeness","display_name":"Completeness (order theory)","score":0.3418999910354614},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3359000086784363},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.32519999146461487}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8817999958992004},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7419999837875366},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.49129998683929443},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.45489999651908875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.453900009393692},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42340001463890076},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3792000114917755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36469998955726624},{"id":"https://openalex.org/C17231256","wikidata":"https://www.wikidata.org/wiki/Q5156540","display_name":"Completeness (order theory)","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.32519999146461487},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.25769999623298645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228001","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228001","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":30,"referenced_works":["https://openalex.org/W1966832848","https://openalex.org/W2047643928","https://openalex.org/W2948982773","https://openalex.org/W2962914239","https://openalex.org/W2963061824","https://openalex.org/W2963351448","https://openalex.org/W3034314048","https://openalex.org/W3035682985","https://openalex.org/W3147184966","https://openalex.org/W3166166117","https://openalex.org/W3169077988","https://openalex.org/W3169651898","https://openalex.org/W3204520143","https://openalex.org/W3206170757","https://openalex.org/W4200597473","https://openalex.org/W4214694907","https://openalex.org/W4293518844","https://openalex.org/W4297828509","https://openalex.org/W4312605624","https://openalex.org/W4320476215","https://openalex.org/W4376626035","https://openalex.org/W4386065385","https://openalex.org/W4386065608","https://openalex.org/W4386065890","https://openalex.org/W4386071651","https://openalex.org/W4386113288","https://openalex.org/W4390706284","https://openalex.org/W4390874575","https://openalex.org/W4393147759","https://openalex.org/W4404612908"],"related_works":[],"abstract_inverted_index":{"Despite":[0],"significant":[1,224],"advances":[2],"in":[3,207,226],"unsupervised":[4],"anomaly":[5,13,62,72,82,233],"detection,":[6],"two":[7],"critical":[8],"challenges":[9],"persist.":[10],"First,":[11],"current":[12],"simulation":[14,73],"methods":[15],"generate":[16],"unrealistic":[17],"anomalies":[18,130],"due":[19],"to":[20,33,42,80,84,128],"insufficient":[21],"constraints":[22],"on":[23,186,198],"anomalous":[24],"regions,":[25,88],"compromising":[26],"training":[27,92],"data":[28,212],"quality.":[29],"Second,":[30],"the":[31,164],"necessity":[32],"train":[34],"separate":[35],"models":[36,79],"for":[37,143],"different":[38],"object":[39],"classes":[40],"leads":[41],"prohibitive":[43],"computational":[44],"and":[45,148,156,182,194,214,230],"storage":[46],"costs,":[47],"severely":[48],"limiting":[49],"practical":[50,215],"deployment.":[51],"To":[52],"address":[53],"these":[54],"issues,":[55],"we":[56],"propose":[57],"a":[58,70,95,110,134,149,223],"novel":[59],"unified":[60],"multi-class":[61,107,170,232],"detection":[63,108,171,234],"framework":[64,174,221],"leveraging":[65],"vision":[66,77],"foundation":[67,78],"models:":[68],"(1)":[69],"foreground-constrained":[71],"method":[74,202],"that":[75,104,120,137,153],"utilizes":[76],"restrict":[81],"generation":[83],"semantically":[85],"meaningful":[86],"foreground":[87],"producing":[89],"more":[90],"realistic":[91],"samples;":[93],"(2)":[94],"Unified":[96],"Anomaly":[97],"Detection":[98],"Model":[99],"(UADM)":[100],"built":[101],"upon":[102],"CLIP":[103],"enables":[105],"efficient":[106],"with":[109,178,190],"single":[111],"model.":[112],"UADM":[113],"incorporates":[114],"three":[115],"components:":[116],"an":[117],"adapter":[118],"module":[119,152],"processes":[121],"multi-scale":[122],"features":[123],"from":[124],"CLIP\u2019s":[125],"intermediate":[126],"layers":[127],"capture":[129],"at":[131],"various":[132],"granularities,":[133],"memory":[135],"bank":[136],"efficiently":[138],"stores":[139],"normal":[140],"pattern":[141],"representations":[142],"rapid":[144],"comparison":[145],"during":[146],"inference,":[147],"Semantic":[150],"Cluster":[151],"effectively":[154],"aggregates":[155],"distinguishes":[157],"anomaly-specific":[158],"semantic":[159],"information.":[160],"Extensive":[161],"evaluations":[162],"demonstrate":[163],"superiority":[165],"of":[166],"our":[167,173,201,220],"approach.":[168],"In":[169],"scenarios,":[172],"achieves":[175],"competitive":[176],"performance":[177,205],"98.5%":[179],"image-level":[180,192],"AUROC":[181,185,193,197],"97.1%":[183],"pixel-level":[184,196],"MVTec":[187],"AD,":[188],"along":[189],"95.3%":[191],"98.4%":[195],"VisA.":[199],"Remarkably,":[200],"maintains":[203],"exceptional":[204],"even":[206],"few-shot":[208],"settings,":[209],"underscoring":[210],"its":[211],"efficiency":[213],"utility.":[216],"These":[217],"results":[218],"establish":[219],"as":[222],"advancement":[225],"developing":[227],"efficient,":[228],"accurate,":[229],"generalizable":[231],"systems.":[235]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-14T00:00:00"}
