{"id":"https://openalex.org/W4414538324","doi":"https://doi.org/10.1109/access.2025.3614713","title":"AULoRA: Anomaly Understanding With Low-Rank Adaptation for Zero-Shot Anomaly Detection","display_name":"AULoRA: Anomaly Understanding With Low-Rank Adaptation for Zero-Shot Anomaly Detection","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414538324","doi":"https://doi.org/10.1109/access.2025.3614713"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3614713","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3614713","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3614713","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109211224","display_name":"Seung-Hyun Oh","orcid":null},"institutions":[{"id":"https://openalex.org/I124633538","display_name":"University of Seoul","ror":"https://ror.org/05en5nh73","country_code":"KR","type":"education","lineage":["https://openalex.org/I124633538"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Seunghyun Oh","raw_affiliation_strings":["Department of Artificial Intelligence, Machine Intelligence Laboratory, University of Seoul, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence, Machine Intelligence Laboratory, University of Seoul, Seoul, South Korea","institution_ids":["https://openalex.org/I124633538"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060855103","display_name":"Seongsu Lee","orcid":"https://orcid.org/0000-0003-3749-3905"},"institutions":[{"id":"https://openalex.org/I124633538","display_name":"University of Seoul","ror":"https://ror.org/05en5nh73","country_code":"KR","type":"education","lineage":["https://openalex.org/I124633538"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seongsu Lee","raw_affiliation_strings":["Department of Artificial Intelligence, Machine Intelligence Laboratory, University of Seoul, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence, Machine Intelligence Laboratory, University of Seoul, Seoul, South Korea","institution_ids":["https://openalex.org/I124633538"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064378252","display_name":"Sehyun Chae","orcid":null},"institutions":[{"id":"https://openalex.org/I124633538","display_name":"University of Seoul","ror":"https://ror.org/05en5nh73","country_code":"KR","type":"education","lineage":["https://openalex.org/I124633538"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seunghye Chae","raw_affiliation_strings":["Department of Artificial Intelligence, Machine Intelligence Laboratory, University of Seoul, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence, Machine Intelligence Laboratory, University of Seoul, Seoul, South Korea","institution_ids":["https://openalex.org/I124633538"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078628073","display_name":"Youngmin Ro","orcid":"https://orcid.org/0000-0002-0661-9192"},"institutions":[{"id":"https://openalex.org/I124633538","display_name":"University of Seoul","ror":"https://ror.org/05en5nh73","country_code":"KR","type":"education","lineage":["https://openalex.org/I124633538"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngmin Ro","raw_affiliation_strings":["Department of Artificial Intelligence, Machine Intelligence Laboratory, University of Seoul, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence, Machine Intelligence Laboratory, University of Seoul, Seoul, South Korea","institution_ids":["https://openalex.org/I124633538"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5109211224"],"corresponding_institution_ids":["https://openalex.org/I124633538"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14452768,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"170095","last_page":"170105"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9980999827384949,"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":0.9980999827384949,"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/T11216","display_name":"Radiation Detection and Scintillator Technologies","score":0.9659000039100647,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9581999778747559,"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":0.8827000260353088},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.652999997138977},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6527000069618225},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5223000049591064},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5217999815940857},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.47769999504089355},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4717999994754791},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.46470001339912415}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8827000260353088},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7688000202178955},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.652999997138977},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6527000069618225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5435000061988831},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5223000049591064},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5217999815940857},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.47769999504089355},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4717999994754791},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.46470001339912415},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43689998984336853},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.38109999895095825},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.35440000891685486},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32690000534057617},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29649999737739563},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26750001311302185},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.25920000672340393}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3614713","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3614713","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3332f8d391444c718293f0df3e058423","is_oa":true,"landing_page_url":"https://doaj.org/article/3332f8d391444c718293f0df3e058423","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 170095-170105 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3614713","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3614713","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2285968993","https://openalex.org/W2914570111","https://openalex.org/W2948982773","https://openalex.org/W2963045681","https://openalex.org/W2964137095","https://openalex.org/W3034314048","https://openalex.org/W3147184966","https://openalex.org/W3165252795","https://openalex.org/W3166166117","https://openalex.org/W3169077988","https://openalex.org/W3169651898","https://openalex.org/W3198377975","https://openalex.org/W3209793239","https://openalex.org/W4200434100","https://openalex.org/W4214694907","https://openalex.org/W4292851291","https://openalex.org/W4306369316","https://openalex.org/W4312310776","https://openalex.org/W4312570668","https://openalex.org/W4312605624","https://openalex.org/W4312651322","https://openalex.org/W4312772600","https://openalex.org/W4319299987","https://openalex.org/W4386065385","https://openalex.org/W4386065890","https://openalex.org/W4386071547","https://openalex.org/W4386075837","https://openalex.org/W4390872773","https://openalex.org/W4402703054","https://openalex.org/W4402716302","https://openalex.org/W4402783842","https://openalex.org/W4402961681","https://openalex.org/W4403780904","https://openalex.org/W4405101982","https://openalex.org/W4413156845"],"related_works":[],"abstract_inverted_index":{"Zero-Shot":[0],"Anomaly":[1],"Detection":[2],"(ZSAD)":[3],"aims":[4],"to":[5,23,31,38,99,131,153],"identify":[6],"anomalies":[7],"in":[8],"unseen":[9],"categories":[10],"or":[11],"scenarios.":[12],"Recently,":[13],"Vision-Language":[14],"Models":[15],"(VLMs),":[16],"most":[17,104],"notably":[18],"CLIP,":[19,169],"have":[20],"been":[21],"utilized":[22],"enhance":[24],"anomaly":[25,66,115,157,179],"detection":[26,116,180],"performance.":[27],"However,":[28],"CLIP":[29],"struggles":[30],"capture":[32],"local":[33],"anomalies,":[34],"which":[35,125],"has":[36],"led":[37],"the":[39,84,103,143,151,162],"development":[40],"of":[41,111,168],"additional":[42],"modules":[43],"that":[44,64],"significantly":[45],"increase":[46],"model":[47,152],"complexity":[48],"and":[49,78,122,156,165,177],"computational":[50],"overhead.":[51],"To":[52,134],"address":[53],"this":[54],"challenge,":[55],"we":[56,93,137],"propose":[57],"<italic":[58],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[59],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">AULoRA</i>,":[60],"a":[61],"novel":[62],"approach":[63],"enhances":[65],"understanding":[67],"by":[68],"integrating":[69],"Low":[70],"Rank":[71],"Adaptation":[72],"(LoRA)":[73],"into":[74,83],"CLIP\u2019s":[75,89],"visual":[76,81,91,144],"encoder":[77],"efficiently":[79],"injecting":[80],"context":[82,145],"textual":[85,139],"representation.":[86],"While":[87],"preserving":[88],"general":[90],"knowledge,":[92],"utilize":[94],"Singular":[95],"Value":[96],"Decomposition":[97],"(SVD)":[98],"selectively":[100],"fine-tune":[101],"only":[102],"relevant":[105],"singular":[106],"components,":[107],"enabling":[108],"precise":[109],"identification":[110],"semantic":[112],"anomalies.":[113],"Nevertheless,":[114],"often":[117],"requires":[118],"capturing":[119],"highly":[120],"diverse":[121,183],"category-specific":[123],"characteristics,":[124],"simple":[126],"text":[127],"prompts":[128],"alone":[129],"struggle":[130],"represent":[132],"adequately.":[133],"overcome":[135],"this,":[136],"adapt":[138],"representations":[140],"based":[141],"on":[142,174],"extracted":[146],"from":[147],"input":[148],"images,":[149],"allowing":[150],"achieve":[154],"category-aware":[155],"sensitive":[158],"alignment.":[159],"AULoRA":[160],"maintains":[161],"original":[163],"architecture":[164],"inference":[166],"efficiency":[167],"while":[170],"achieving":[171],"state-of-the-art":[172],"performance":[173],"both":[175],"image-level":[176],"pixel-level":[178],"benchmarks":[181],"across":[182],"industrial":[184],"datasets.":[185]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}