Developing Deep Learning Models for Multimedia Applications in TensorFlow
Authors: 
Antonio José G. Busson, 
Lucas Caracas de Figueiredo, Gabriel N. P. dos Santos, André Luiz de B. Damasceno, Sérgio Colcher, Ruy MilidiúAuthors Info & Claims
Antonio José G. Busson, Lucas Caracas de Figueiredo, Gabriel N. P. dos Santos, André Luiz de B. Damasceno, Sérgio Colcher, Ruy MilidiúAuthors Info & ClaimsPages 7 - 9
Abstract
Methods based on Deep Learning became state-of-the-art in several Multimedia challenges. However, there is a gap of professionals to perform Deep Learning in the industry. Therefore, this short course aims to present the grounds and ways to develop multimedia applications using methods based on Deep Learning. Likewise, this short course is an opportunity for students and IT professionals can qualify yourselves.
References
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Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018).
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Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition. 815--823.
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Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi. 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, Vol. 4. 12.
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Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2818--2826.
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Index Terms
- Developing Deep Learning Models for Multimedia Applications in TensorFlow
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Information & Contributors
Information
Published In
October 2018
437 pages
ISBN:9781450358675
DOI:10.1145/3243082
- General Chairs:
- Manoel Carvalho Marques Neto,
- Renato Lima Novais,
- Carlos Ferraz,
- Windson Viana
Copyright © 2018 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
In-Cooperation
- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SBC: Brazilian Computer Society
- SIGMM: ACM Special Interest Group on Multimedia
- CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
- CGIBR: Comite Gestor da Internet no Brazil
- CAPES: Brazilian Higher Education Funding Council
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 October 2018
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Author Tags
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- Course
- Research
- Refereed limited
Conference
WebMedia '18
WebMedia '18: Brazilian Symposium on Multimedia and the Web
October 16 - 19, 2018
BA, Salvador, Brazil
Acceptance Rates
WebMedia '18 Paper Acceptance Rate 37 of 111 submissions, 33%;
Overall Acceptance Rate 270 of 873 submissions, 31%
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