{"id":"https://openalex.org/W4389156690","doi":"https://doi.org/10.48550/arxiv.2311.16514","title":"Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach","display_name":"Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach","publication_year":2023,"publication_date":"2023-11-27","ids":{"openalex":"https://openalex.org/W4389156690","doi":"https://doi.org/10.48550/arxiv.2311.16514"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2311.16514","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.16514","pdf_url":"https://arxiv.org/pdf/2311.16514","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2311.16514","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031097265","display_name":"Ayush K. Rai","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rai, Ayush K.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108920185","display_name":"Tarun Krishna","orcid":"https://orcid.org/0009-0008-4196-0610"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krishna, Tarun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048948657","display_name":"Feiyan Hu","orcid":"https://orcid.org/0000-0001-7451-6438"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Feiyan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043850369","display_name":"Alexandru Dr\u00eemb\u0103rean","orcid":"https://orcid.org/0000-0001-6265-4905"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Drimbarean, Alexandru","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073924795","display_name":"Kevin McGuinness","orcid":"https://orcid.org/0000-0003-1336-6477"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"McGuinness, Kevin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008334059","display_name":"Alan F. Smeaton","orcid":"https://orcid.org/0000-0003-1028-8389"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Smeaton, Alan F.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5106498523","display_name":"Noel E. O\u2019Connor","orcid":"https://orcid.org/0000-0002-4033-9135"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"O'Connor, Noel E.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5031097265"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":1.0,"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":1.0,"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/T12391","display_name":"Artificial Immune Systems Applications","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9905999898910522,"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":0.788075864315033},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.73458331823349},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7227301597595215},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6496304869651794},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6016033887863159},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5823149681091309},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5573498606681824},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.5287829637527466},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.453213632106781},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.425995796918869},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3578488826751709},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33147555589675903},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3227275609970093},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2599032521247864},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07925647497177124}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.788075864315033},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.73458331823349},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7227301597595215},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6496304869651794},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6016033887863159},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5823149681091309},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5573498606681824},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.5287829637527466},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.453213632106781},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.425995796918869},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3578488826751709},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33147555589675903},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3227275609970093},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2599032521247864},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07925647497177124},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2311.16514","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.16514","pdf_url":"https://arxiv.org/pdf/2311.16514","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2311.16514","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2311.16514","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":"pmh:oai:arXiv.org:2311.16514","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.16514","pdf_url":"https://arxiv.org/pdf/2311.16514","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389156690.pdf","grobid_xml":"https://content.openalex.org/works/W4389156690.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W3194885736","https://openalex.org/W3046391934","https://openalex.org/W4363671829","https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037"],"abstract_inverted_index":{"Video":[0],"Anomaly":[1],"Detection":[2],"(VAD)":[3],"is":[4,10,22],"an":[5,76,102],"open-set":[6],"recognition":[7],"task,":[8],"which":[9],"usually":[11],"formulated":[12],"as":[13],"a":[14,87,97,105,129],"one-class":[15],"classification":[16],"(OCC)":[17],"problem,":[18],"where":[19],"training":[20],"data":[21,31,51],"comprised":[23],"of":[24,44,63,67,101,145,198],"videos":[25],"with":[26,59,176],"normal":[27,34,50],"instances":[28],"while":[29],"test":[30],"contains":[32],"both":[33],"and":[35,52,65,110,153,167,182,196],"anomalous":[36],"instances.":[37],"Recent":[38],"works":[39],"have":[40],"investigated":[41],"the":[42,49,113,123,138,187,194],"creation":[43],"pseudo-anomalies":[45],"(PAs)":[46],"using":[47,104,116],"only":[48],"making":[53],"strong":[54],"assumptions":[55],"about":[56,73],"real-world":[57,135,208],"anomalies":[58,74,136,209],"regards":[60],"to":[61,69,118,133],"abnormality":[62],"objects":[64],"speed":[66],"motion":[68],"inject":[70],"prior":[71],"information":[72],"in":[75,122],"autoencoder":[77],"(AE)":[78],"based":[79,184],"reconstruction":[80,149,183],"model":[81],"during":[82],"training.":[83],"This":[84],"work":[85],"proposes":[86],"novel":[88],"method":[89,172],"for":[90],"generating":[91],"generic":[92],"spatio-temporal":[93,120],"PAs":[94,180,199],"by":[95,141,206],"inpainting":[96],"masked":[98],"out":[99],"region":[100],"image":[103],"pre-trained":[106],"Latent":[107],"Diffusion":[108],"Model":[109],"further":[111],"perturbing":[112],"optical":[114],"flow":[115],"mixup":[117],"emulate":[119],"distortions":[121],"data.":[124],"In":[125],"addition,":[126],"we":[127],"present":[128],"simple":[130],"unified":[131],"framework":[132],"detect":[134],"under":[137,186],"OCC":[139,188],"setting":[140],"learning":[142],"three":[143],"types":[144],"anomaly":[146],"indicators,":[147],"namely":[148,163],"quality,":[150],"temporal":[151],"irregularity":[152],"semantic":[154],"inconsistency.":[155],"Extensive":[156],"experiments":[157],"on":[158,174],"four":[159],"VAD":[160],"benchmark":[161],"datasets":[162],"Ped2,":[164],"Avenue,":[165],"ShanghaiTech":[166],"UBnormal":[168],"demonstrate":[169],"that":[170],"our":[171],"performs":[173],"par":[175],"other":[177],"existing":[178],"state-of-the-art":[179],"generation":[181],"methods":[185],"setting.":[189],"Our":[190],"analysis":[191],"also":[192],"examines":[193],"transferability":[195],"generalisation":[197],"across":[200],"these":[201],"datasets,":[202],"offering":[203],"valuable":[204],"insights":[205],"identifying":[207],"through":[210],"PAs.":[211]},"counts_by_year":[],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2023-11-30T00:00:00"}
