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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3S3CTGB
Repositorysid.inpe.br/sibgrapi/2018/10.17.11.39
Last Update2018:10.17.11.39.27 kovaleski@poli.ufrj.br
Metadatasid.inpe.br/sibgrapi/2018/10.17.11.39.27
Metadata Last Update2020:02.20.22.06.49 administrator
Citation KeyKovaleskiNuneSilv:2018:CoDeCo
TitleComparison of deep convolutional networks for action recognition in videos
FormatOn-line
Year2018
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 02
Number of Files1
Size542 KiB
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Author1 Kovaleski, Patrícia de Andrade
2 Nunes, Leonardo de Oliveira
3 Silva, Eduardo Antônio Barros da
Affiliation1 Federal University of Rio de Janeiro
2 Microsoft
3 Federal University of Rio de Janeiro
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addresskovaleski@poli.ufrj.br
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-17 11:39:27 :: kovaleski@poli.ufrj.br -> administrator ::
2020-02-20 22:06:49 :: administrator -> :: 2018
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Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Transferable1
Tertiary TypeUndergraduate Work
Keywordsaction recognition, deep convolutional networks, deep learning.
AbstractThis work presents the implementation of deep convolutional networks for action recognition in videos based on the well-known two-stream architecture, that is composed of a temporal and a spatial stream. The development was done in order to replicate the one reported in the original paper using the Microsoft Cognitive Toolkit (CNTK). Different experiments were made in order to evaluate the performance of the two-stream in a public dataset when trained for different base network architectures and input data modality.
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Languageen
Target Filecomparison-deep-convolutional-final.pdf
User Groupkovaleski@poli.ufrj.br
Visibilityshown
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume

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