@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 = "29 Oct.-1 Nov. 2018",
language = "en",
ibi = "8JMKD3MGPAW/3S3CTGB",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3S3CTGB",
targetfile = "comparison-deep-convolutional-final.pdf",
urlaccessdate = "2024, Apr. 29"
}