{"id":"https://openalex.org/W4376852321","doi":"https://doi.org/10.1145/3573942.3574104","title":"Multi-instance learning anomaly event detection based on Transformer","display_name":"Multi-instance learning anomaly event detection based on Transformer","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4376852321","doi":"https://doi.org/10.1145/3573942.3574104"},"language":"en","primary_location":{"id":"doi:10.1145/3573942.3574104","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573942.3574104","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","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/A5009075946","display_name":"Qin Fei-fei","orcid":"https://orcid.org/0000-0001-8279-4075"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feifei Qin","raw_affiliation_strings":["School of Computer, Xi'an University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0001-8279-4075","affiliations":[{"raw_affiliation_string":"School of Computer, Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076911583","display_name":"Yuelei Xiao","orcid":"https://orcid.org/0000-0001-9516-1947"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuelei Xiao","raw_affiliation_strings":["School of Modern Posts, Xi'an University of Posts and Telecommunications, China and \rBig Data Industry Application Research Department, Shaanxi Provincial Information Engineering Research Institute, China"],"raw_orcid":"https://orcid.org/0000-0001-9516-1947","affiliations":[{"raw_affiliation_string":"School of Modern Posts, Xi'an University of Posts and Telecommunications, China and \rBig Data Industry Application Research Department, Shaanxi Provincial Information Engineering Research Institute, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5009075946"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19004359,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"846","last_page":"851"},"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":0.9975000023841858,"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":0.9879999756813049,"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/computer-science","display_name":"Computer science","score":0.7760074138641357},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6662504076957703},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6658840775489807},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6242688894271851},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47349295020103455},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.442375123500824},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4395650327205658},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08977371454238892}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7760074138641357},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6662504076957703},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6658840775489807},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6242688894271851},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47349295020103455},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.442375123500824},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4395650327205658},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08977371454238892},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3573942.3574104","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573942.3574104","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1553303721","display_name":null,"funder_award_id":"No. 2018YFC08242- 04","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G3927037657","display_name":null,"funder_award_id":"No.2016KTTSGY01- 03","funder_id":"https://openalex.org/F4320336571","funder_display_name":"Shaanxi Science and Technology Co-ordination and Innovation Project of China"},{"id":"https://openalex.org/G7284184339","display_name":null,"funder_award_id":"No.61741216, 61402367","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320336571","display_name":"Shaanxi Science and Technology Co-ordination and Innovation Project of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2341058432","https://openalex.org/W2579718262","https://openalex.org/W2619947201","https://openalex.org/W2921491036","https://openalex.org/W2963524571","https://openalex.org/W2963610939","https://openalex.org/W2963795951","https://openalex.org/W2970271202","https://openalex.org/W2981741013","https://openalex.org/W3018665999","https://openalex.org/W3034544290","https://openalex.org/W3035021504","https://openalex.org/W3089682612","https://openalex.org/W3094502228","https://openalex.org/W3096609285","https://openalex.org/W3136793533","https://openalex.org/W3138516171","https://openalex.org/W3162784145","https://openalex.org/W3198471657","https://openalex.org/W3210206767","https://openalex.org/W4206784731","https://openalex.org/W4287728020","https://openalex.org/W4288347855"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2105642232","https://openalex.org/W3197833032","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W4386081464","https://openalex.org/W4294635752","https://openalex.org/W3207332793","https://openalex.org/W4380075502"],"abstract_inverted_index":{"Multi-instance":[0],"learning":[1,80],"(MIL)":[2],"is":[3,157],"the":[4,19,41,59,65,106,111,118,126,131,151],"dominant":[5],"approach":[6],"for":[7,64,135],"weakly":[8],"supervised":[9],"anomaly":[10,81,119],"detection":[11,47,83],"in":[12,165],"surveillance":[13],"videos.":[14],"The":[15,138],"shortcomings":[16],"of":[17,43,61,115,154],"using":[18],"features":[20,38,104],"extracted":[21],"by":[22],"networks":[23],"such":[24],"as":[25],"Convolutional":[26],"3D":[27],"(C3D)":[28],"or":[29],"inflated":[30,93],"3D-ConvNet":[31],"(I3D)":[32],"alone":[33],"to":[34,58,97,109],"extract":[35,98,110],"video":[36],"context":[37],"have":[39],"prompted":[40],"emergence":[42],"various":[44],"abnormal":[45],"event":[46,82],"algorithms":[48],"based":[49,85],"on":[50,86,141],"attention":[51],"mechanisms.":[52],"Vision":[53],"Transformer":[54],"(ViT)":[55],"applies":[56],"transformer":[57],"field":[60],"computer":[62],"vision":[63],"first":[66],"time":[67],"and":[68,101,117,130,147],"demonstrates":[69],"its":[70],"superior":[71],"performance.":[72],"In":[73],"this":[74],"paper,":[75],"we":[76,124],"propose":[77],"a":[78],"multi-instance":[79],"method":[84,156],"Transformer,":[87],"called":[88],"MIL-ViT,":[89],"which":[90],"uses":[91],"an":[92],"I3D":[94],"pre-training":[95],"model":[96],"Spatio-temporal":[99],"features,":[100],"then":[102],"inputs":[103],"into":[105],"ViT":[107],"encoder":[108],"particular":[112],"salient":[113],"pieces":[114],"information,":[116],"scores":[120],"are":[121],"obtained.":[122],"Furthermore,":[123],"introduce":[125],"MIL":[127],"ranking":[128],"loss":[129,133],"center":[132],"function":[134],"better":[136],"training.":[137],"experimental":[139],"results":[140],"two":[142],"benchmark":[143],"datasets":[144],"(i.e.":[145],"ShanghaiTech":[146],"UCF-Crime)":[148],"show":[149],"that":[150],"AUC":[152],"value":[153],"our":[155],"significantly":[158],"improved":[159],"compared":[160],"with":[161],"several":[162],"state-of-the-art":[163],"methods":[164],"recent":[166],"years.":[167]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}