{"id":"https://openalex.org/W7140237208","doi":"https://doi.org/10.48550/arxiv.2603.21562","title":"Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection","display_name":"Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140237208","doi":"https://doi.org/10.48550/arxiv.2603.21562"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.21562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21562","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.21562","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zhou, Mingle","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Mingle","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Liu, Jiahui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jiahui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wan, Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wan, Jin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Li, Gang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Gang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Li, Min","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Min","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9858999848365784,"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":0.9858999848365784,"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":0.002199999988079071,"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":0.0017999999690800905,"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":0.8356000185012817},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5497000217437744},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5340999960899353},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4593999981880188},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4535999894142151},{"id":"https://openalex.org/keywords/novelty-detection","display_name":"Novelty detection","score":0.4203000068664551},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40299999713897705}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8356000185012817},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7925000190734863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6642000079154968},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5497000217437744},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5340999960899353},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4593999981880188},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45410001277923584},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4535999894142151},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.4203000068664551},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40299999713897705},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.39820000529289246},{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2718999981880188},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.21562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21562","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.21562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21562","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.4353524148464203,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Unsupervised":[0,21],"Continuous":[1],"Anomaly":[2,22],"Detection":[3,23],"(UCAD)":[4],"is":[5],"gaining":[6],"attention":[7],"for":[8],"effectively":[9],"addressing":[10],"the":[11,39],"catastrophic":[12],"forgetting":[13],"and":[14,83,91,130,138,152],"heavy":[15],"computational":[16],"burden":[17],"issues":[18],"in":[19,43,50,67],"traditional":[20],"(UAD).":[24],"However,":[25],"existing":[26],"UCAD":[27],"approaches":[28],"that":[29,80,112,142],"rely":[30],"solely":[31],"on":[32,135,149],"visual":[33,90],"information":[34],"are":[35],"insufficient":[36],"to":[37,125],"capture":[38],"manifold":[40],"of":[41,101],"normality":[42],"complex":[44],"scenes,":[45],"thereby":[46],"impeding":[47],"further":[48],"gains":[49],"anomaly":[51,63],"detection":[52,64,128],"accuracy.":[53],"To":[54],"overcome":[55],"this":[56],"limitation,":[57],"we":[58,71,104],"propose":[59],"an":[60,114],"unsupervised":[61],"continual":[62],"framework":[65],"grounded":[66],"multimodal":[68],"prompting.":[69],"Specifically,":[70],"introduce":[72],"a":[73,98,106,120],"Continual":[74],"Multimodal":[75],"Prompt":[76],"Memory":[77],"Bank":[78],"(CMPMB)":[79],"progressively":[81],"distills":[82],"retains":[84],"prototypical":[85],"normal":[86],"patterns":[87],"from":[88],"both":[89],"textual":[92],"domains":[93],"across":[94],"consecutive":[95],"tasks,":[96],"yielding":[97],"richer":[99],"representation":[100],"normality.":[102],"Furthermore,":[103],"devise":[105],"Defect-Semantic-Guided":[107],"Adaptive":[108,115],"Fusion":[109,122],"Mechanism":[110],"(DSG-AFM)":[111],"integrates":[113],"Normalization":[116],"Module":[117],"(ANM)":[118],"with":[119],"Dynamic":[121],"Strategy":[123],"(DFS)":[124],"jointly":[126],"enhance":[127],"accuracy":[129],"adversarial":[131],"robustness.":[132],"Benchmark":[133],"experiments":[134],"MVTec":[136],"AD":[137],"VisA":[139],"datasets":[140],"show":[141],"our":[143],"approach":[144],"achieves":[145],"state-of-the-art":[146],"(SOTA)":[147],"performance":[148],"image-level":[150],"AUROC":[151],"pixel-level":[153],"AUPR":[154],"metrics.":[155]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-25T00:00:00"}