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@InProceedings{KovaleskiNuneSilv:2018:CoDeCo,
               author = "Kovaleski, Patr{\'{\i}}cia de Andrade and Nunes, Leonardo de 
                         Oliveira and Silva, Eduardo Ant{\^o}nio Barros da",
          affiliation = "{Federal University of Rio de Janeiro} and Microsoft and {Federal 
                         University of Rio de Janeiro}",
                title = "Comparison of deep convolutional networks for action recognition 
                         in videos",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "action recognition, deep convolutional networks, deep learning.",
             abstract = "This 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.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
                  ibi = "8JMKD3MGPAW/3S3CTGB",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3S3CTGB",
           targetfile = "comparison-deep-convolutional-final.pdf",
        urlaccessdate = "2020, Dec. 04"
}


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