{"id":"https://openalex.org/W7138180034","doi":"https://doi.org/10.1609/aaai.v40i26.39319","title":"Harnessing Vision-Language Models for Time Series Anomaly Detection","display_name":"Harnessing Vision-Language Models for Time Series Anomaly Detection","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138180034","doi":"https://doi.org/10.1609/aaai.v40i26.39319"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i26.39319","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i26.39319","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39319/43280","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39319/43280","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122996789","display_name":"Zelin He","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zelin He","raw_affiliation_strings":["Pennsylvania State University"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065291315","display_name":"Sarah Alnegheimish","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sarah Alnegheimish","raw_affiliation_strings":["Massachusetts Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129719690","display_name":"Matthew Reimherr","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Reimherr","raw_affiliation_strings":["Pennsylvania State University\nAmazon"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University\nAmazon","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5122996789"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39668094,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"26","first_page":"21690","last_page":"21698"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7634000182151794,"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":0.7634000182151794,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.10779999941587448,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.02500000037252903,"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":0.7149999737739563},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6075000166893005},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5821999907493591},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5699999928474426},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.4779999852180481},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4740000069141388},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4609000086784363},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.44339999556541443}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.738099992275238},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7149999737739563},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6075000166893005},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5821999907493591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5803999900817871},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5699999928474426},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.4779999852180481},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4740000069141388},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4609000086784363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44339999556541443},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.44339999556541443},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.3849000036716461},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32030001282691956},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.27730000019073486},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2702000141143799},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2687999904155731}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i26.39319","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i26.39319","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39319/43280","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i26.39319","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i26.39319","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39319/43280","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138180034.pdf","grobid_xml":"https://content.openalex.org/works/W7138180034.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Time-series":[0],"anomaly":[1],"detection":[2,142],"(TSAD)":[3],"has":[4,79],"played":[5],"a":[6,10,52,97,103,108,127,167],"vital":[7],"role":[8],"in":[9,163,170,194],"variety":[11],"of":[12,66,91],"fields,":[13],"including":[14],"healthcare,":[15],"finance,":[16],"and":[17,85,135,160,187],"sensor-based":[18],"condition":[19],"monitoring.":[20],"Prior":[21],"methods,":[22],"which":[23,114],"mainly":[24],"focus":[25],"on":[26,30,55,82,107,189],"training":[27],"domain-specific":[28],"models":[29,58],"numerical":[31],"data,":[32],"lack":[33],"the":[34,64,89,141,144,174],"visual\u2013temporal":[35],"reasoning":[36,70,137],"capacity":[37,138],"that":[38,130,151],"human":[39],"experts":[40],"have":[41,62],"to":[42,76,120,139],"identify":[43],"contextual":[44],"anomalies.":[45],"To":[46,87],"fill":[47],"this":[48],"gap,":[49],"we":[50,95],"explore":[51],"solution":[53],"based":[54],"vision":[56,112],"language":[57,183],"(VLMs).":[59],"Recent":[60],"studies":[61],"shown":[63],"ability":[65],"VLMs":[67,92],"for":[68,93],"visual":[69],"tasks,":[71],"yet":[72],"their":[73],"direct":[74],"application":[75],"time":[77,117],"series":[78,118],"fallen":[80],"short":[81],"both":[83],"accuracy":[84],"efficiency.":[86],"harness":[88],"power":[90],"TSAD,":[94],"propose":[96],"two-stage":[98],"solution,":[99],"with":[100],"(1)":[101],"ViT4TS,":[102],"vision-screening":[104],"stage":[105,129],"built":[106],"relatively":[109],"lightweight":[110],"pre-trained":[111,159],"encoder,":[113],"leverages":[115],"2-D":[116],"representations":[119],"accurately":[121],"localize":[122],"candidate":[123],"anomalies;":[124],"(2)":[125],"VLM4TS,":[126],"VLM-based":[128],"integrates":[131],"global":[132],"temporal":[133],"context":[134],"VLM's":[136],"refine":[140],"upon":[143],"candidates":[145],"provided":[146],"by":[147],"ViT4TS.":[148],"We":[149],"show":[150],"without":[152],"any":[153],"time-series":[154,158],"training,":[155],"VLM4TS":[156,178],"outperforms":[157,181],"from-scratch":[161],"baselines":[162],"most":[164],"cases,":[165],"yielding":[166],"24.6%":[168],"improvement":[169],"F1-max":[171],"score":[172],"over":[173],"best":[175],"baseline.":[176],"Moreover,":[177],"also":[179],"consistently":[180],"existing":[182],"model-based":[184],"TSAD":[185],"methods":[186],"is":[188],"average":[190],"36\u00d7":[191],"more":[192],"efficient":[193],"token":[195],"usage.":[196]},"counts_by_year":[],"updated_date":"2026-03-20T20:47:17.329874","created_date":"2026-03-18T00:00:00"}
