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Reference TypeConference Paper (Conference Proceedings)
Last Update2017:
Metadata Last Update2020: administrator
Citation KeyPontiRibNazBuiCol:2017:EvYoWa
TitleEverything you wanted to know about Deep Learning for Computer Vision but were afraid to ask
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size1708 KiB
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Author1 Ponti, Moacir A.
2 Ribeiro, Leonardo S. F.
3 Nazaré, Tiago S.
4 Bui, Tu
5 Collomosse, John
Affiliation1 Universidade de São Paulo
2 Universidade de São Paulo
3 Universidade de São Paulo
4 University of Surrey
5 University of Surrey
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeTutorial
History2017-09-05 22:09:43 :: -> administrator ::
2020-02-20 22:06:47 :: administrator -> :: 2017
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Is the master or a copy?is the master
Content Stagecompleted
KeywordsComputer Vision, Deep Learning, Image Processing, Video Processing.
AbstractDeep 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.
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