{"id":"https://openalex.org/W7146986088","doi":"https://doi.org/10.1109/icaiic68212.2026.11454213","title":"S2VAD: A Self-Supervised Framework for Unsupervised Visual Anomaly Detection","display_name":"S2VAD: A Self-Supervised Framework for Unsupervised Visual Anomaly Detection","publication_year":2026,"publication_date":"2026-02-24","ids":{"openalex":"https://openalex.org/W7146986088","doi":"https://doi.org/10.1109/icaiic68212.2026.11454213"},"language":null,"primary_location":{"id":"doi:10.1109/icaiic68212.2026.11454213","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic68212.2026.11454213","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","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/A5009942977","display_name":"Mariam Ishtiaq","orcid":"https://orcid.org/0000-0003-1450-4149"},"institutions":[{"id":"https://openalex.org/I4210161173","display_name":"Korea Railroad Research Institute","ror":"https://ror.org/04gzcxt97","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4210161173","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Mariam Ishtiaq","raw_affiliation_strings":["Korea Railroad Research Institute (KRRI),Railroad Physical AI Research Department,Uiwang,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Railroad Research Institute (KRRI),Railroad Physical AI Research Department,Uiwang,South Korea","institution_ids":["https://openalex.org/I4210161173"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103743312","display_name":"JongUn Won","orcid":null},"institutions":[{"id":"https://openalex.org/I4210161173","display_name":"Korea Railroad Research Institute","ror":"https://ror.org/04gzcxt97","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4210161173","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongun Won","raw_affiliation_strings":["Korea Railroad Research Institute (KRRI),Railroad Physical AI Research Department,Uiwang,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Railroad Research Institute (KRRI),Railroad Physical AI Research Department,Uiwang,South Korea","institution_ids":["https://openalex.org/I4210161173"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210161173"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28489733,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"375","last_page":"380"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9459999799728394,"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":0.9459999799728394,"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":0.011500000022351742,"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":0.009800000116229057,"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/anomaly-detection","display_name":"Anomaly detection","score":0.8371999859809875},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6287999749183655},{"id":"https://openalex.org/keywords/normality","display_name":"Normality","score":0.508400022983551},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4433000087738037},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.44179999828338623},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4316999912261963},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4036000072956085},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.375900000333786}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8371999859809875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.664900004863739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.659500002861023},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6287999749183655},{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.508400022983551},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4433000087738037},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.44179999828338623},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4316999912261963},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4036000072956085},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.361299991607666},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3598000109195709},{"id":"https://openalex.org/C180462255","wikidata":"https://www.wikidata.org/wiki/Q3559736","display_name":"Standard test image","level":4,"score":0.3425999879837036},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3248000144958496},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.28439998626708984},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.275299996137619},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.2700999975204468},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.26100000739097595},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26100000739097595}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaiic68212.2026.11454213","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic68212.2026.11454213","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322096","display_name":"Korea Railroad Research Institute","ror":"https://ror.org/04gzcxt97"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W3118600296","https://openalex.org/W4308235797","https://openalex.org/W4386065385","https://openalex.org/W4386065608","https://openalex.org/W4386065890","https://openalex.org/W4390779522","https://openalex.org/W4393147759","https://openalex.org/W4402264592","https://openalex.org/W4402915567","https://openalex.org/W4406012864","https://openalex.org/W4406354830","https://openalex.org/W4408230264","https://openalex.org/W4409356433","https://openalex.org/W4409917254","https://openalex.org/W4411310919","https://openalex.org/W4413156235","https://openalex.org/W4416798111"],"related_works":[],"abstract_inverted_index":{"In":[0],"open":[1],"world":[2],"environments,":[3],"vision":[4],"anomaly":[5,16,31,67],"detection":[6,32,68],"(VAD)":[7],"is":[8],"inherently":[9],"complex":[10],"due":[11],"to":[12,109],"diverse":[13],"and":[14,90,113],"unpredictable":[15],"manifestations.":[17],"To":[18,137],"overcome":[19],"the":[20,23,57,73,106,110,117,139],"limitation":[21],"of":[22,25,75,134],"availability":[24],"an":[26,78,131],"inclusive":[27],"training":[28],"data,":[29],"unsupervised":[30,66],"(UAD)":[33],"methods":[34],"provide":[35],"a":[36,62,84,87,91,100],"solid":[37],"baseline.":[38],"Principally,":[39],"using":[40,94],"only":[41],"normal":[42,81],"distribution":[43],"patterns":[44],"for":[45,65,80,96,104],"training,":[46],"any":[47],"deviations":[48],"can":[49],"be":[50],"flagged":[51],"as":[52,77,86],"anomalies":[53],"during":[54],"inference.":[55],"Following":[56],"same":[58],"analogy,":[59],"we":[60,142],"propose":[61],"self-supervised":[63],"framework":[64],"termed":[69],"S2VAD.":[70],"We":[71,98],"explore":[72,138],"potential":[74],"DINOv2":[76],"encoder":[79],"feature":[82],"extraction,":[83],"bottleneck":[85],"compression":[88],"head,":[89],"transformer":[92],"decoder":[93],"self-attention":[95],"VAD.":[97],"use":[99],"global":[101],"cosine":[102],"loss":[103],"comparing":[105],"test":[107],"image":[108,132],"learned":[111],"normality":[112],"detecting":[114],"anomalies.":[115],"Using":[116],"benchmark":[118],"MVTec-AD":[119],"dataset,":[120],"our":[121],"work":[122,146],"shows":[123],"state-of-the-art":[124],"(SOTA)":[125],"performance":[126],"on":[127],"texture":[128],"classes":[129],"with":[130],"AUROC":[133],"99.8":[135],"%.":[136],"model":[140],"further,":[141],"share":[143],"interesting":[144],"future":[145],"directions.":[147]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-04-02T00:00:00"}
