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.

本论文提出了新的一致性策略，包括自监督学习和半监督学习，以提高基于像素级运动描述能力，并在 KITTI-2015 景流基准测试中达到卓越的结果，实现前景准确性最佳的成果，其中且仅使用单眼图像输入。