Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians

  title={Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians},
  author={Peiyun Hu and Deva Ramanan},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
Convolutional neural nets (CNNs) have demonstrated remarkable performance in recent history. Such approaches tend to work in a "unidirectional" bottom-up feed-forward fashion. However, practical experience and biological evidence tells us that feedback plays a crucial role, particularly for detailed spatial understanding tasks. This work explores "bidirectional" architectures that also reason with top-down feedback: neural units are influenced by both lower and higher-level units. We do so by… CONTINUE READING
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