@InProceedings{PontiRibNazBuiCol:2017:EvYoWa,
author = "Ponti, Moacir A. and Ribeiro, Leonardo S. F. and Nazar{\'e},
Tiago S. and Bui, Tu and Collomosse, John",
affiliation = "{Universidade de S{\~a}o Paulo} and {Universidade de S{\~a}o
Paulo} and {Universidade de S{\~a}o Paulo} and {University of
Surrey} and {University of Surrey}",
title = "Everything you wanted to know about Deep Learning for Computer
Vision but were afraid to ask",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "Computer Vision, Deep Learning, Image Processing, Video
Processing.",
abstract = "Deep Learning methods are currently the state-of-the-art in many
Computer Vision and Image Processing problems, in particular image
classification. After years of intensive investigation, a few
models matured and became important tools, including Convolutional
Neural Networks (CNNs), Siamese and Triplet Networks,
Auto-Encoders (AEs) and Generative Adversarial Networks (GANs).
The field is fast-paced and there is a lot of terminologies to
catch up for those who want to adventure in Deep Learning waters.
This paper has the objective to introduce the most fundamental
concepts of Deep Learning for Computer Vision in particular CNNs,
AEs and GANs, including architectures, inner workings and
optimization. We offer an updated description of the theoretical
and practical knowledge of working with those models. After that,
we describe Siamese and Triplet Networks, not often covered in
tutorial papers, as well as review the literature on recent and
exciting topics such as visual stylization, pixel-wise prediction
and video processing. Finally, we discuss the limitations of Deep
Learning for Computer Vision.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
language = "en",
ibi = "8JMKD3MGPAW/3PJAABB",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PJAABB",
targetfile = "
_2017_sibgrapi__Tutorial_Deep_Learning_for_CV___Survey_Paper_CRP.pdf",
urlaccessdate = "2024, May 02"
}