  Imposing consistency through proxy tasks has been shown to enhance
data-driven learning and enable self-supervision in various tasks. This paper
introduces novel and effective consistency strategies for optical flow
estimation, a problem where labels from real-world data are very challenging to
derive. More specifically, we propose occlusion consistency and zero forcing in
the forms of self-supervised learning and transformation consistency in the
form of semi-supervised learning. We apply these consistency techniques in a
way that the network model learns to describe pixel-level motions better while
requiring no additional annotations. We demonstrate that our consistency
strategies applied to a strong baseline network model using the original
datasets and labels provide further improvements, attaining the
state-of-the-art results on the KITTI-2015 scene flow benchmark in the
non-stereo category. Our method achieves the best foreground accuracy (4.33% in
Fl-all) over both the stereo and non-stereo categories, even though using only
monocular image inputs.
