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Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PJAABB
Repositorysid.inpe.br/sibgrapi/2017/09.05.22.09
Last Update2017:09.05.22.09.43 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/09.05.22.09.43
Metadata Last Update2022:05.18.22.18.25 (UTC) administrator
Citation KeyPontiRibNazBuiCol:2017:EvYoWa
TitleEverything you wanted to know about Deep Learning for Computer Vision but were afraid to ask
FormatOn-line
Year2017
Access Date2024, May 02
Number of Files1
Size1708 KiB
2. Context
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
e-Mail Addressmoacir@icmc.usp.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeTutorial
History (UTC)2017-09-05 22:09:43 :: moacir@icmc.usp.br -> administrator ::
2022-05-18 22:18:25 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
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.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2017 > Everything you wanted...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PJAABB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PJAABB
Languageen
Target File_2017_sibgrapi__Tutorial_Deep_Learning_for_CV___Survey_Paper_CRP.pdf
User Groupmoacir@icmc.usp.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 7
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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