  Many computer vision systems require low-cost segmentation algorithms based
on deep learning, either because of the enormous size of input images or
limited computational budget. Common solutions uniformly downsample the input
images to meet memory constraints, assuming all pixels are equally informative.
In this work, we demonstrate that this assumption can harm the segmentation
performance because the segmentation difficulty varies spatially. We combat
this problem by introducing a learnable downsampling module, which can be
optimised together with the given segmentation model in an end-to-end fashion.
We formulate the problem of training such downsampling module as optimisation
of sampling density distributions over the input images given their
low-resolution views. To defend against degenerate solutions (e.g.
over-sampling trivial regions like the backgrounds), we propose a
regularisation term that encourages the sampling locations to concentrate
around the object boundaries. We find the downsampling module learns to sample
more densely at difficult locations, thereby improving the segmentation
performance. Our experiments on benchmarks of high-resolution street view,
aerial and medical images demonstrate substantial improvements in terms of
efficiency-and-accuracy trade-off compared to both uniform downsampling and two
recent advanced downsampling techniques.

Computer Vision and Pattern Recognition;Machine Learning