  The growing use of convolutional neural networks (CNN) for a broad range of
visual tasks, including tasks involving fine details, raises the problem of
applying such networks to a large field of view, since the amount of
computations increases significantly with the number of pixels. To deal
effectively with this difficulty, we develop and compare methods of using CNNs
for the task of small target localization in natural images, given a limited
"budget" of samples to form an image. Inspired in part by human vision, we
develop and compare variable sampling schemes, with peak resolution at the
center and decreasing resolution with eccentricity, applied iteratively by
re-centering the image at the previous predicted target location. The results
indicate that variable resolution models significantly outperform constant
resolution models. Surprisingly, variable resolution models and in particular
multi-channel models, outperform the optimal, "budget-free" full-resolution
model, using only 5\% of the samples.
