{"id":"https://openalex.org/W4409262082","doi":"https://doi.org/10.1109/wacv61041.2025.00706","title":"Heterogeneous Datasets for Unsupervised Image Anomaly Detection","display_name":"Heterogeneous Datasets for Unsupervised Image Anomaly Detection","publication_year":2025,"publication_date":"2025-02-26","ids":{"openalex":"https://openalex.org/W4409262082","doi":"https://doi.org/10.1109/wacv61041.2025.00706"},"language":"en","primary_location":{"id":"doi:10.1109/wacv61041.2025.00706","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61041.2025.00706","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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/A5042699821","display_name":"Juan Lagos","orcid":null},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Juan Lagos","raw_affiliation_strings":["Tampere University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tampere University","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haider Ali","orcid":null},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Haider Ali","raw_affiliation_strings":["Tampere University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tampere University","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117075916","display_name":"Adnan Faroque","orcid":null},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Adnan Faroque","raw_affiliation_strings":["Tampere University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tampere University","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088180438","display_name":"Esa Rahtu","orcid":"https://orcid.org/0000-0001-8767-0864"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Esa Rahtu","raw_affiliation_strings":["Tampere University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tampere University","institution_ids":["https://openalex.org/I166825849"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I166825849"],"apc_list":null,"apc_paid":null,"fwci":1.673,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.7966863,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"7266","last_page":"7276"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9983000159263611,"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":0.9983000159263611,"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":0.9466000199317932,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9279000163078308,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.722357988357544},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6843857765197754},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5367807745933533},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5149417519569397},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47346702218055725}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.722357988357544},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6843857765197754},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5367807745933533},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5149417519569397},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47346702218055725}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61041.2025.00706","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61041.2025.00706","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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":31,"referenced_works":["https://openalex.org/W1958236864","https://openalex.org/W1994257249","https://openalex.org/W2007339694","https://openalex.org/W2108598243","https://openalex.org/W2122646361","https://openalex.org/W2138011018","https://openalex.org/W2148583977","https://openalex.org/W2767106145","https://openalex.org/W2768800090","https://openalex.org/W2946466522","https://openalex.org/W2948982773","https://openalex.org/W2964032056","https://openalex.org/W3013207792","https://openalex.org/W3034986516","https://openalex.org/W3044215473","https://openalex.org/W3114010851","https://openalex.org/W3184778778","https://openalex.org/W3186300274","https://openalex.org/W3204520143","https://openalex.org/W3209793239","https://openalex.org/W4212874935","https://openalex.org/W4224941647","https://openalex.org/W4285147180","https://openalex.org/W4308235797","https://openalex.org/W4312239247","https://openalex.org/W4312605624","https://openalex.org/W4312772600","https://openalex.org/W4360584477","https://openalex.org/W4382458928","https://openalex.org/W4390874575","https://openalex.org/W4404612908"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Unsupervised":[0],"anomaly":[1,65,150,164],"detection":[2,102],"(AD)":[3],"is":[4],"a":[5],"critical":[6],"task":[7],"in":[8,64,149,179,195],"various":[9,49],"domains,":[10],"from":[11,57],"manufacturing":[12],"to":[13,29,100,140],"infrastructure":[14],"monitoring.":[15],"To":[16],"advance":[17],"this":[18,196],"field,":[19,197],"we":[20],"introduce":[21],"two":[22],"novel":[23],"datasets:":[24],"CARS-AD":[25,42,135,188,215],"and":[26,38,74,82,103,119,128,189,213,217],"ROADS-AD,":[27],"designed":[28],"challenge":[30],"existing":[31],"unsupervised":[32,90],"AD":[33,85,91,124,203],"methods":[34,92,204],"with":[35,48,210],"their":[36,138],"diverse":[37,163],"heterogeneous":[39,177],"image":[40],"content.":[41],"comprises":[43],"real":[44],"images":[45,54],"of":[46,55,84,116,159,171,187,201,206],"cars":[47],"defects,":[50],"while":[51],"ROADS-AD":[52,154,190,218],"contains":[53],"roads":[56],"multiple":[58],"countries,":[59],"each":[60],"presenting":[61],"unique":[62],"challenges":[63,174],"detection.":[66],"These":[67],"datasets":[68,118,178],"provide":[69],"ground":[70,76],"truth":[71,77],"pixel-wise":[72],"masks":[73],"image-level":[75],"labels,":[78],"enabling":[79],"detailed":[80],"evaluation":[81],"benchmarking":[83],"algorithms.":[86],"We":[87,182],"evaluate":[88],"state-of-the-art":[89],"on":[93,133,152],"both":[94],"datasets,":[95],"using":[96],"the":[97,114,117,120,134,153,157,169,173,185,199],"AUROC":[98],"metric":[99],"assess":[101],"localization":[104,151],"performance.":[105],"Our":[106,166],"results":[107],"reveal":[108],"significant":[109],"room":[110],"for":[111,122,162],"improvement,":[112],"underscoring":[113],"complexity":[115],"need":[121],"robust":[123],"techniques.":[125],"Notably,":[126],"Csflow":[127],"U-Flow":[129],"demonstrate":[130],"superior":[131],"performance":[132],"Dataset,":[136,155],"leveraging":[137],"ability":[139],"process":[141],"multi-scale":[142],"features":[143],"effectively.":[144],"Conversely,":[145],"Reverse":[146],"Distillation":[147],"excels":[148],"showcasing":[156],"importance":[158,170],"nuanced":[160],"approaches":[161],"types.":[165],"findings":[167],"underscore":[168],"addressing":[172],"posed":[175],"by":[176],"unsu-pervised":[180],"AD.":[181],"hope":[183],"that":[184],"introduction":[186],"will":[191],"inspire":[192],"further":[193],"research":[194],"driving":[198],"development":[200],"innovative":[202],"capa-ble":[205],"handling":[207],"real-world":[208],"anomalies":[209],"greater":[211],"accuracy":[212],"reliability.":[214],"Dataset":[216,219],"are":[220],"publicly":[221],"available":[222],"at":[223],"https://github.com/juanb09111/heterogeneousAD.":[224]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}