  The primary aim of this manuscript is to underscore a significant limitation
in current deep learning models, particularly vision models. Unlike human
vision, which efficiently selects only the essential visual areas for further
processing, leading to high speed and low energy consumption, deep vision
models process the entire image. In this work, we examine this issue from a
broader perspective and propose a solution that could pave the way for the next
generation of more efficient vision models. Basically, convolution and pooling
operations are selectively applied to altered regions, with a change map sent
to subsequent layers. This map indicates which computations need to be
repeated. The code is available at
https://github.com/aliborji/spatial_attention.
