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Friday, January 20, 2017
Deep Learning for Image and Video Processing
This event is hosted/sponsored by IEEE SPS Chapter.
Dr. Jonathon Shlens, Google Research
Dr. George Toderici, Google Research
AMD Commons Auditorium, 991 Stewart Dr., Sunnyvale, CA (map or Google Maps)
$10 for IEEE members with SPS membership (join SPS now for additional $22 only and save)
$30 for IEEE members (join now and save $25 off IEEE membership fee)
$50 for Others
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degree program at local universities and colleges. To apply for free admission, please send
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Deep learning has profoundly changed the field of computer vision in the last few years. Many computer vision problems have been recast with techniques from deep learning and in turn achieved state of the art results and become industry standards. In this tutorial we will provide an overview about the central ideas of deep learning as applied to computer vision. In the course of this tutorial we will survey the many applications of deep learning to image and video problems. The goal of this tutorial is to teach the central and core ideas and provide a high level overview of how deep learning has influenced computer vision.
- Motivations for deep learning in computer vision.
- Recent progress in applying deep learning for vision.
- Architectures for image classification and image regression.
- Survey of image recognition and localization techniques.
- Tools for performing deep learning
- Advances in image synthesis and image compression.
- Architectures for video classification and summarization.
Jonathon Shlens received his Ph.D in computational neuroscience from UC San Diego in 2007 where his research focused on applying machine learning towards understanding visual processing in real biological systems. He was previously a research fellow at the Howard Hughes Medical Institute, a research engineer at Pixar Animation Studios and a Miller Fellow at UC Berkeley. He has been at Google Research since 2010 and is currently a research scientist focused on building scalable vision systems. During his time at Google, he has been a core contributor to deep learning systems including the recently open-sourced TensorFlow. His research interests have spanned the development of state-of-the-art image recognition systems and training algorithms for deep networks.
George Toderici received his Ph.D. in Computer Science from the University of Houston in 2007 where his research focused on 2D-to-3D face recognition, and joined Google in 2008. His current work at Google Research is focused on lossy multimedia compression using neural networks. His past projects include the design of neural-network architectures and various classical approaches for video classification, YouTube channel recommendations, and video enhancement.
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