{"id":"https://openalex.org/W4221002311","doi":"https://doi.org/10.1109/tcsii.2022.3161061","title":"Collaborative Normality Learning Framework for Weakly Supervised Video Anomaly Detection","display_name":"Collaborative Normality Learning Framework for Weakly Supervised Video Anomaly Detection","publication_year":2022,"publication_date":"2022-03-22","ids":{"openalex":"https://openalex.org/W4221002311","doi":"https://doi.org/10.1109/tcsii.2022.3161061"},"language":"en","primary_location":{"id":"doi:10.1109/tcsii.2022.3161061","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsii.2022.3161061","pdf_url":null,"source":{"id":"https://openalex.org/S93916849","display_name":"IEEE Transactions on Circuits & Systems II Express Briefs","issn_l":"1549-7747","issn":["1549-7747","1558-3791"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems II: Express Briefs","raw_type":"journal-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/A5100355803","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-1312-0146"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yang Liu","raw_affiliation_strings":["Academy for Engineering and Technology, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-1312-0146","affiliations":[{"raw_affiliation_string":"Academy for Engineering and Technology, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374987","display_name":"Jing Liu","orcid":"https://orcid.org/0000-0002-2819-0200"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Liu","raw_affiliation_strings":["Academy for Engineering and Technology, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-2819-0200","affiliations":[{"raw_affiliation_string":"Academy for Engineering and Technology, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066706009","display_name":"Mengyang Zhao","orcid":"https://orcid.org/0000-0001-8322-0479"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengyang Zhao","raw_affiliation_strings":["Academy for Engineering and Technology, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-8322-0479","affiliations":[{"raw_affiliation_string":"Academy for Engineering and Technology, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100415901","display_name":"Shuang Li","orcid":"https://orcid.org/0000-0002-1172-4595"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuang Li","raw_affiliation_strings":["Institute of Molecular Plus, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-1172-4595","affiliations":[{"raw_affiliation_string":"Institute of Molecular Plus, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058393974","display_name":"Liang Song","orcid":"https://orcid.org/0000-0002-8143-9052"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Song","raw_affiliation_strings":["Academy for Engineering and Technology, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Academy for Engineering and Technology, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100355803"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":8.7344,"has_fulltext":false,"cited_by_count":63,"citation_normalized_percentile":{"value":0.98095078,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"69","issue":"5","first_page":"2508","last_page":"2512"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9957000017166138,"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"}},{"id":"https://openalex.org/T12391","display_name":"Artificial Immune Systems Applications","score":0.9781000018119812,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7400349378585815},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7380809783935547},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7173095941543579},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6823798418045044},{"id":"https://openalex.org/keywords/normality","display_name":"Normality","score":0.6808794140815735},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6287088990211487},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5748661160469055},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.47255226969718933},{"id":"https://openalex.org/keywords/clips","display_name":"CLIPS","score":0.45446789264678955},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.42456576228141785},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3957098722457886},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.38821884989738464},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1276334822177887},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08198085427284241}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7400349378585815},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7380809783935547},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7173095941543579},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6823798418045044},{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.6808794140815735},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6287088990211487},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5748661160469055},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.47255226969718933},{"id":"https://openalex.org/C2778739407","wikidata":"https://www.wikidata.org/wiki/Q165372","display_name":"CLIPS","level":2,"score":0.45446789264678955},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.42456576228141785},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3957098722457886},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.38821884989738464},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1276334822177887},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08198085427284241},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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":1,"locations":[{"id":"doi:10.1109/tcsii.2022.3161061","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsii.2022.3161061","pdf_url":null,"source":{"id":"https://openalex.org/S93916849","display_name":"IEEE Transactions on Circuits & Systems II Express Briefs","issn_l":"1549-7747","issn":["1549-7747","1558-3791"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems II: Express Briefs","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W1522301498","https://openalex.org/W1522734439","https://openalex.org/W1931450083","https://openalex.org/W2126579184","https://openalex.org/W2341058432","https://openalex.org/W2752782242","https://openalex.org/W2753526808","https://openalex.org/W2777342313","https://openalex.org/W2921491036","https://openalex.org/W2962716148","https://openalex.org/W2963524571","https://openalex.org/W2963610939","https://openalex.org/W2963795951","https://openalex.org/W2964350391","https://openalex.org/W2970271202","https://openalex.org/W2987228832","https://openalex.org/W3010937474","https://openalex.org/W3014352273","https://openalex.org/W3035021504","https://openalex.org/W3035240825","https://openalex.org/W3101466234","https://openalex.org/W3108027406","https://openalex.org/W3139230390","https://openalex.org/W3157370245","https://openalex.org/W3168600998","https://openalex.org/W3168984673","https://openalex.org/W6600983433","https://openalex.org/W6631190155","https://openalex.org/W6766008006","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W2417253731","https://openalex.org/W2350469024","https://openalex.org/W2491583298","https://openalex.org/W2036154621","https://openalex.org/W2327827625","https://openalex.org/W2395860100","https://openalex.org/W1936207157","https://openalex.org/W3186512740","https://openalex.org/W4363671829","https://openalex.org/W2780476542"],"abstract_inverted_index":{"Video":[0],"anomaly":[1,111],"detection":[2],"(VAD)":[3],"under":[4],"weak":[5],"supervision":[6],"aims":[7],"to":[8,29,41,58,76,85,123,138,170],"temporally":[9],"locate":[10],"abnormal":[11,72,92,107,152],"clips":[12,105],"using":[13],"the":[14,23,30,60,69,82,87,91,96,100,104,115,125,133,146,164,171,175,181],"easy-to-obtain":[15],"video-level":[16],"labels.":[17],"In":[18],"this":[19],"brief,":[20],"we":[21],"introduce":[22],"underlying":[24],"thought":[25],"of":[26,64,90,99,142,149,177,183],"unsupervised":[27,56,129],"VAD":[28,33],"weakly":[31,134],"supervised":[32,135],"and":[34,71,121,151],"propose":[35],"a":[36,48,78],"collaborative":[37,184],"normality":[38,185],"learning":[39],"framework":[40,166],"obtain":[42],"more":[43,154],"discriminative":[44],"deep":[45,49],"representations.":[46],"Specifically,":[47],"auto-encoder":[50,130],"is":[51,84],"first":[52],"trained":[53,126],"in":[54,106],"an":[55,110],"manner":[57],"learn":[59],"prototypical":[61,140],"spatial-temporal":[62],"patterns":[63],"normal":[65,70,101,120,143,150],"videos.":[66,102],"Then,":[67],"both":[68],"videos":[73,93,108],"are":[74,117],"used":[75,122],"train":[77],"regression":[79,136],"module,":[80],"where":[81],"objective":[83],"make":[86],"average":[88,116],"score":[89,98,112],"higher":[94],"than":[95,114],"maximum":[97],"Finally,":[103],"with":[109,132],"lower":[113],"regarded":[118],"as":[119],"fine-tune":[124],"auto-encoder.":[127],"The":[128],"collaborates":[131],"model":[137],"extract":[139],"features":[141,148],"clips,":[144],"making":[145],"learned":[147],"events":[153],"distinguishable.":[155],"Experimental":[156],"results":[157,176],"on":[158],"three":[159],"benchmark":[160],"datasets":[161],"show":[162],"that":[163],"proposed":[165],"achieves":[167],"comparable":[168],"performance":[169],"state-of-the-art":[172],"methods.":[173],"Additionally,":[174],"ablation":[178],"studies":[179],"demonstrate":[180],"validity":[182],"learning.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":25},{"year":2022,"cited_by_count":9}],"updated_date":"2026-05-02T08:42:23.175194","created_date":"2025-10-10T00:00:00"}