publication . Preprint . 2014

Recurrent Models of Visual Attention

Mnih, Volodymyr; Heess, Nicolas; Graves, Alex; Kavukcuoglu, Koray;
Open Access English
  • Published: 24 Jun 2014
Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent neural network model that is capable of extracting information from an image or video by adaptively selecting a sequence of regions or locations and only processing the selected regions at high resolution. Like convolutional neural networks, the proposed model has a degree of translation invariance built-in, but the amount of computation it performs can be controlled independently of the input image size. While the model is non-differentiable, it can be trained using re...
arXiv: Computer Science::Computer Vision and Pattern Recognition
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
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