  Deep learning approaches to optical flow estimation have seen rapid progress
over the recent years. One common trait of many networks is that they refine an
initial flow estimate either through multiple stages or across the levels of a
coarse-to-fine representation. While leading to more accurate results, the
downside of this is an increased number of parameters. Taking inspiration from
both classical energy minimization approaches as well as residual networks, we
propose an iterative residual refinement (IRR) scheme based on weight sharing
that can be combined with several backbone networks. It reduces the number of
parameters, improves the accuracy, or even achieves both. Moreover, we show
that integrating occlusion prediction and bi-directional flow estimation into
our IRR scheme can further boost the accuracy. Our full network achieves
state-of-the-art results for both optical flow and occlusion estimation across
several standard datasets.
