{"id":"https://openalex.org/W2507847939","doi":"https://doi.org/10.1109/icme.2016.7552981","title":"Multimedia event detection via deep spatial-temporal neural networks","display_name":"Multimedia event detection via deep spatial-temporal neural networks","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2507847939","doi":"https://doi.org/10.1109/icme.2016.7552981","mag":"2507847939"},"language":"en","primary_location":{"id":"doi:10.1109/icme.2016.7552981","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2016.7552981","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5012800942","display_name":"Jingyi Hou","orcid":"https://orcid.org/0000-0003-0172-3115"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyi Hou","raw_affiliation_strings":["Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing, P.R. China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011070646","display_name":"Xinxiao Wu","orcid":"https://orcid.org/0000-0002-2056-6947"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinxiao Wu","raw_affiliation_strings":["Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing, P.R. China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091498417","display_name":"Feiwu Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feiwu Yu","raw_affiliation_strings":["Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing, P.R. China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100731042","display_name":"Yunde Jia","orcid":"https://orcid.org/0000-0003-1900-8945"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunde Jia","raw_affiliation_strings":["Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing, P.R. China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9986000061035156,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9984999895095825,"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/computer-science","display_name":"Computer science","score":0.8335598707199097},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.752360463142395},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6931594610214233},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6880544424057007},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6451351642608643},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.6293491125106812},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5833092927932739},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5469737648963928},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5326805710792542},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5272554159164429},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4904157817363739},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4808564782142639},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4473855495452881}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8335598707199097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.752360463142395},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6931594610214233},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6880544424057007},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6451351642608643},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6293491125106812},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5833092927932739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5469737648963928},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5326805710792542},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5272554159164429},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4904157817363739},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4808564782142639},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4473855495452881},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"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/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme.2016.7552981","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2016.7552981","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W318042436","https://openalex.org/W581956982","https://openalex.org/W1606347560","https://openalex.org/W1670387562","https://openalex.org/W1686810756","https://openalex.org/W1871385855","https://openalex.org/W1900913856","https://openalex.org/W1923404803","https://openalex.org/W1924770834","https://openalex.org/W1944615693","https://openalex.org/W1947481528","https://openalex.org/W1950136256","https://openalex.org/W2012592962","https://openalex.org/W2024197741","https://openalex.org/W2064675550","https://openalex.org/W2067646051","https://openalex.org/W2103924867","https://openalex.org/W2105101328","https://openalex.org/W2108598243","https://openalex.org/W2127069950","https://openalex.org/W2141939040","https://openalex.org/W2142521298","https://openalex.org/W2155893237","https://openalex.org/W2156303437","https://openalex.org/W2951552696","https://openalex.org/W2964227963","https://openalex.org/W6616837769","https://openalex.org/W6636358008","https://openalex.org/W6637342671","https://openalex.org/W6637373629","https://openalex.org/W6639141586","https://openalex.org/W6640212811","https://openalex.org/W6640257725","https://openalex.org/W6675402254","https://openalex.org/W6676297131","https://openalex.org/W6682864246"],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W2354198838","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W1989130879","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2103419012","https://openalex.org/W2761785940"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,41,46,86,106],"novel":[4],"method":[5,118,142],"using":[6,76],"deep":[7,13,78],"spatial-temporal":[8],"neural":[9,43,48,52,70],"networks":[10,37],"based":[11],"on":[12,134],"Convolutional":[14],"Neural":[15,59],"Network":[16],"(CNN)":[17],"for":[18,102,130],"multimedia":[19],"event":[20,103],"detection.":[21],"To":[22],"sufficiently":[23],"take":[24],"advantage":[25],"of":[26,32,64,88,125,149],"the":[27,62,77,82,95,113,147,150],"motion":[28,55],"and":[29,45,97,127],"appearance":[30],"information":[31,56,74],"events":[33],"from":[34],"videos,":[35],"our":[36,141],"contain":[38],"two":[39,123],"branches:":[40],"temporal":[42,51,96],"network":[44,53,71],"spatial":[47,69,98],"network.":[49],"The":[50,68],"captures":[54],"by":[57,75],"Recurrent":[58],"Networks":[60],"with":[61,90],"mutation":[63],"gated":[65],"recurrent":[66],"unit.":[67],"catches":[72],"object":[73],"CNN,":[79],"to":[80,119],"encode":[81],"CNN":[83],"features":[84,99,129],"as":[85],"bag":[87],"semantics":[89],"more":[91],"discriminative":[92],"representations.":[93],"Both":[94],"are":[100],"beneficial":[101],"detection":[104],"in":[105],"fully":[107],"coupled":[108],"way.":[109],"Finally,":[110],"we":[111],"employ":[112],"generalized":[114],"multiple":[115],"kernel":[116],"learning":[117],"effectively":[120],"fuse":[121],"these":[122],"types":[124],"heterogeneous":[126],"complementary":[128],"action":[131],"recognition.":[132],"Experiments":[133],"TRECVID":[135],"MEDTest":[136],"14":[137],"dataset":[138],"show":[139],"that":[140],"achieves":[143],"better":[144],"performance":[145],"than":[146],"state":[148],"art.":[151]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}