  We propose a novel representation for dense pixel-wise estimation tasks using
CNNs that boosts accuracy and reduces training time, by explicitly exploiting
joint coarse-and-fine reasoning. The coarse reasoning is performed over a
discrete classification space to obtain a general rough solution, while the
fine details of the solution are obtained over a continuous regression space.
In our approach both components are jointly estimated, which proved to be
beneficial for improving estimation accuracy. Additionally, we propose a new
network architecture, which combines coarse and fine components by treating the
fine estimation as a refinement built on top of the coarse solution, and
therefore adding details to the general prediction. We apply our approach to
the challenging problem of optical flow estimation and empirically validate it
against state-of-the-art CNN-based solutions trained from scratch and tested on
large optical flow datasets.
