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Jose Alvarez

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DeepVision: Deep Learning in Computer Vision

The goal of the DeepVision Workshop is to accelerate the study of deep learning algorithms in computer vision problems. With the increase of acceleration of digital photography and the advances in storage devices over the last decade, we have seen explosive growth in the available amount of visual data and equally explosive growth in the computational capacities for image understanding. Instead of hand crafting features, recent advancement in deep learning suggests an emerging approach to extracting useful representations for many computer vision tasks.

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Temporal Deep Learning

Videos contain valuable temporal information that can be exploited to achieve better performance. Exploiting temporal information is of great importance in computer vision applications, like object tracking and recognition, scene analysis and understanding, etc. Deep learning based techniques are challenged to employ temporal information in such applications. Although some advances have been performed in this direction, mainly involving 3D convolutions, motion-based input features, or deep temporalbased models such as RNN-LSTM, significant advances are expected to be performed in this field.

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Paper Submission

Important Dates

  1. Paper Submission: March 31st, 2017
  2. Supplemental Material Submission: March 31st, 2017
  3. Author Notification: April 21st, 2017
  4. Camera Ready: April 28th, 2017

Organizing Commitee