  In this paper, we tackle the challenge of actively attending to visual scenes
using a foveated sensor. We introduce an end-to-end differentiable foveated
active vision architecture that leverages a graph convolutional network to
process foveated images, and a simple yet effective formulation for foveated
image sampling. Our model learns to iteratively attend to regions of the image
relevant for classification. We conduct detailed experiments on a variety of
image datasets, comparing the performance of our method with previous
approaches to foveated vision while measuring how the impact of different
choices, such as the degree of foveation, and the number of fixations the
network performs, affect object recognition performance. We find that our model
outperforms a state-of-the-art CNN and foveated vision architectures of
comparable parameters and a given pixel or computation budget
