{"id":"https://openalex.org/W7138065391","doi":"https://doi.org/10.48550/arxiv.2603.13306","title":"Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervision","display_name":"Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervision","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7138065391","doi":"https://doi.org/10.48550/arxiv.2603.13306"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.13306","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13306","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.13306","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114316560","display_name":"Kirill Borodin","orcid":"https://orcid.org/0009-0001-8203-1059"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Borodin, Kirill","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112038813","display_name":"Kirill Kondrashov","orcid":"https://orcid.org/0009-0009-5385-4636"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kondrashov, Kirill","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129671151","display_name":"Nikita Vasiliev","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vasiliev, Nikita","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120325768","display_name":"Ksenia Gladkova","orcid":"https://orcid.org/0009-0008-9961-6708"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gladkova, Ksenia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120325769","display_name":"Inna Larina","orcid":"https://orcid.org/0009-0009-8477-150X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Larina, Inna","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063243000","display_name":"Mikhail Gorodnichev","orcid":"https://orcid.org/0000-0003-1739-9831"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gorodnichev, Mikhail","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5041288941","display_name":"Grach Mkrtchian","orcid":"https://orcid.org/0000-0002-5802-5513"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mkrtchian, Grach","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5114316560"],"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":0.8528000116348267,"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":0.8528000116348267,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.010499999858438969,"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":0.009100000374019146,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.8306000232696533},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.7132999897003174},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5544000267982483},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.53329998254776},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4104999899864197},{"id":"https://openalex.org/keywords/multiple-models","display_name":"Multiple Models","score":0.4049000144004822},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.3700999915599823},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.33059999346733093}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8306000232696533},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7132999897003174},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6632999777793884},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5544000267982483},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.5371999740600586},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.53329998254776},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4553000032901764},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4104999899864197},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.4049000144004822},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3935999870300293},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.3700999915599823},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3172000050544739},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3165999948978424},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.28769999742507935},{"id":"https://openalex.org/C46637626","wikidata":"https://www.wikidata.org/wiki/Q6693015","display_name":"Low latency (capital markets)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.2572999894618988},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.13306","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13306","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.13306","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13306","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"CCTV":[0],"safety":[1],"monitoring":[2],"demands":[3],"anomaly":[4,127],"detectors":[5,26],"combine":[6],"reliable":[7],"clip-level":[8,126],"accuracy":[9],"with":[10],"predictable":[11],"per-clip":[12,66,95],"latency":[13,67],"despite":[14],"weak":[15],"supervision.":[16],"This":[17],"work":[18],"investigates":[19],"compact":[20,47,78,120],"vision-language":[21],"models":[22],"(VLMs)":[23],"as":[24,124],"practical":[25],"for":[27],"this":[28],"regime.":[29],"A":[30],"unified":[31],"evaluation":[32],"protocol":[33],"standardizes":[34],"preprocessing,":[35],"prompting,":[36],"dataset":[37],"splits,":[38],"metrics,":[39],"and":[40,53,64,73,85,137],"runtime":[41],"settings":[42],"to":[43,68,122],"compare":[44],"parameter-efficiently":[45],"adapted":[46],"VLMs":[48,79,121],"against":[49],"training-free":[50],"VLM":[51],"pipelines":[52],"weakly":[54],"supervised":[55],"baselines.":[56],"Evaluation":[57],"spans":[58],"accuracy,":[59],"precision,":[60],"recall,":[61],"F1,":[62],"ROC-AUC,":[63],"average":[65],"jointly":[69],"quantify":[70],"detection":[71],"quality":[72],"efficiency.":[74],"With":[75],"parameter-efficient":[76,117],"adaptation,":[77],"achieve":[80],"performance":[81],"on":[82],"par":[83],"with,":[84],"in":[86],"several":[87],"cases":[88],"exceeding,":[89],"established":[90],"approaches":[91],"while":[92],"retaining":[93],"competitive":[94],"latency.":[96],"Adaptation":[97],"further":[98],"reduces":[99],"prompt":[100,107],"sensitivity,":[101],"producing":[102],"more":[103],"consistent":[104,138],"behavior":[105],"across":[106],"regimes":[108],"under":[109],"the":[110],"shared":[111],"protocol.":[112],"These":[113],"results":[114],"show":[115],"that":[116],"fine-tuning":[118],"enables":[119],"serve":[123],"dependable":[125],"detectors,":[128],"yielding":[129],"a":[130,135],"favorable":[131],"accuracy-efficiency":[132],"trade-off":[133],"within":[134],"transparent":[136],"experimental":[139],"setup.":[140]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}
