{"id":"https://openalex.org/W4416250473","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227162","title":"Weakly Supervised Video Anomaly Detection Via Contrastive Clustering","display_name":"Weakly Supervised Video Anomaly Detection Via Contrastive Clustering","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250473","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227162"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227162","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227162","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101200815","display_name":"Weihua Meng","orcid":"https://orcid.org/0009-0005-4689-7532"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weihua Meng","raw_affiliation_strings":["Northwestern Polytechnical University,School of Computer Science,China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Computer Science,China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027058453","display_name":"Hengbo Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hengbo Xu","raw_affiliation_strings":["Northwestern Polytechnical University,School of Computer Science,China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Computer Science,China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015584209","display_name":"Qinyi Lv","orcid":"https://orcid.org/0000-0002-5359-5701"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinyi Lv","raw_affiliation_strings":["Northwestern Polytechnical University,School of Electronics and Information,China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Electronics and Information,China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013382259","display_name":"Congqi Cao","orcid":"https://orcid.org/0000-0002-0217-9791"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Congqi Cao","raw_affiliation_strings":["Northwestern Polytechnical University,School of Computer Science,China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Computer Science,China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101590880","display_name":"Yanning Zhang","orcid":"https://orcid.org/0000-0002-3839-2965"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanning Zhang","raw_affiliation_strings":["Northwestern Polytechnical University,School of Computer Science,China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Computer Science,China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101200815"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18226438,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9642999768257141,"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":0.9642999768257141,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.02280000038444996,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11439","display_name":"Video Analysis and Summarization","score":0.0044999998062849045,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/overfitting","display_name":"Overfitting","score":0.7336999773979187},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6592000126838684},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6513000130653381},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5889999866485596},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5548999905586243},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4977000057697296},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.492000013589859},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4253000020980835}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7615000009536743},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7336999773979187},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7204999923706055},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6592000126838684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6513000130653381},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5889999866485596},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5548999905586243},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4977000057697296},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.492000013589859},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4253000020980835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3409999907016754},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.31299999356269836},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.303600013256073},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.2705000042915344}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227162","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227162","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W2921906393","https://openalex.org/W2963524571","https://openalex.org/W2963795951","https://openalex.org/W2987228832","https://openalex.org/W2998702159","https://openalex.org/W3035021504","https://openalex.org/W3089682612","https://openalex.org/W3134566480","https://openalex.org/W3136793533","https://openalex.org/W3168600998","https://openalex.org/W3174421047","https://openalex.org/W4283811196","https://openalex.org/W4304098292","https://openalex.org/W4312550608","https://openalex.org/W4312560592","https://openalex.org/W4312614039","https://openalex.org/W4312660844","https://openalex.org/W4312908541","https://openalex.org/W4382457788","https://openalex.org/W4382461973","https://openalex.org/W4382464562","https://openalex.org/W4386071599","https://openalex.org/W4386071694","https://openalex.org/W4386076087","https://openalex.org/W4386076149","https://openalex.org/W4386159820","https://openalex.org/W4390190911","https://openalex.org/W4393153999","https://openalex.org/W4394598077","https://openalex.org/W4402351117","https://openalex.org/W4402351163"],"related_works":[],"abstract_inverted_index":{"\u2014Weakly":[0],"supervised":[1],"video":[2],"anomaly":[3],"detection":[4],"(WS-VAD)":[5],"is":[6],"challenging":[7],"because":[8],"it":[9],"relies":[10],"on":[11,32,160],"video-level":[12],"binary":[13],"annotations":[14],"to":[15,38,98,121,123,149],"make":[16],"frame-level":[17],"predictions.":[18],"Existing":[19],"methods":[20],"often":[21],"convert":[22],"WSVAD":[23],"into":[24],"a":[25,55,130,140],"multiple":[26],"instance":[27],"learning":[28],"(MIL)":[29],"task,":[30],"focusing":[31],"isolated":[33],"segments":[34],"that":[35,59,145],"contribute":[36],"most":[37],"the":[39,43,61,71,96,101,111,161,167],"classification":[40],"while":[41,83],"neglecting":[42],"temporal":[44],"context":[45],"and":[46,65,75,105,126,152,163],"detailed":[47],"feature":[48],"distinctions.":[49],"In":[50],"this":[51],"paper,":[52],"we":[53,69,109,138],"propose":[54],"contrastive":[56],"clustering":[57,72],"strategy":[58],"enhances":[60],"representation":[62],"of":[63,169],"normal":[64,104,124],"abnormal":[66,106,133],"features.":[67,107],"Specifically,":[68],"treat":[70],"center":[73],"features":[74,84,119,128,148],"their":[76],"corresponding":[77],"categories":[78,87],"as":[79,90],"positive":[80],"sample":[81],"pairs,":[82],"from":[85],"different":[86],"are":[88],"treated":[89],"negative":[91],"samples.":[92],"This":[93],"approach":[94],"enables":[95],"network":[97],"better":[99],"explore":[100],"distinction":[102],"between":[103],"Furthermore,":[108],"address":[110],"bias":[112,131,151],"in":[113],"pre-trained":[114,147],"models,":[115],"where":[116],"I3D":[117],"pre-training":[118],"tend":[120],"overfit":[122],"videos":[125],"CLIP":[127],"exhibit":[129],"towards":[132],"videos.":[134],"To":[135],"mitigate":[136],"this,":[137],"introduce":[139],"simple":[141],"early":[142],"fusion":[143],"method":[144],"combines":[146],"eliminate":[150],"obtain":[153],"more":[154],"comprehensive":[155],"spatio-temporal":[156],"representations.":[157],"Extensive":[158],"experiments":[159],"UCF-Crime":[162],"XD-Violence":[164],"datasets":[165],"demonstrate":[166],"effectiveness":[168],"our":[170],"approach,":[171],"achieving":[172],"state-of-the-art":[173],"performance.":[174]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}