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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3S3CTGB
Repositorysid.inpe.br/sibgrapi/2018/10.17.11.39
Last Update2018:10.17.11.39.27 (UTC) kovaleski@poli.ufrj.br
Metadata Repositorysid.inpe.br/sibgrapi/2018/10.17.11.39.27
Metadata Last Update2022:05.18.22.18.32 (UTC) administrator
Citation KeyKovaleskiNuneSilv:2018:CoDeCo
TitleComparison of deep convolutional networks for action recognition in videos
FormatOn-line
Year2018
Access Date2024, Apr. 28
Number of Files1
Size542 KiB
2. Context
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
Date29 Oct.-1 Nov. 2018
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeUndergraduate Work
History (UTC)2018-10-17 11:39:27 :: kovaleski@poli.ufrj.br -> administrator ::
2022-05-18 22:18:32 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
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.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2018 > Comparison of deep...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3S3CTGB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3S3CTGB
Languageen
Target Filecomparison-deep-convolutional-final.pdf
User Groupkovaleski@poli.ufrj.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 9
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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