What Should We Learn from Deep Learning for Visual Understanding

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What Should We Learn from Deep Learning for Visual Understanding

In the past decade, there has been a transformative and permanent revolution in computer vision cultivated by the reinvigorated adoption of deep learning for visual understanding tasks. Driven by the increasing availability of large annotated data sets, efficient training techniques, and faster computational platforms, deeplearning-based solutions have been progressively employed in a broader spectrum of applications from image classification to activity recognition.

The special issue of IEEE Signal Processing Magazine (SPM) in November 2017 is devoted to providing survey articles on the latest advances in deep learning for visual understanding. Its objective is to encourage a diverse audience of researchers and enthusiasts toward an effective participation in the solution of analogous problems in other signal processing fields by inseminating similar ideas.

The range of articles in this two-part special issue indicates the breadth of the computer vision discipline. (Part two will be published in January 2018.) Many fundamental areas are surveyed from the computer vision perspective, including

  • reinforcement learning
  • learning with limited and no supervision (unsupervised learning)
  • weakly supervised learning
  • zero- and few-shot learning
  • domain adaptation
  • multimodal learning
  • metric learning
  • generative adversarial networks
  • recurrent networks
  • regression with Bayesian networks
  • model compression and robustness.

In addition, in-depth overviews of several deep-learning-based computer vision applications are provided, including

  • inverse problems such as superresolution and image enhancement
  • picture quality prediction
  • saliency detection
  • image and video segmentation with conditional random fields
  • image-to-text generation
  • visual question answering
  • face image analytics.

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