  Feature warping is a core technique in optical flow estimation; however, the
ambiguity caused by occluded areas during warping is a major problem that
remains unsolved. In this paper, we propose an asymmetric occlusion-aware
feature matching module, which can learn a rough occlusion mask that filters
useless (occluded) areas immediately after feature warping without any explicit
supervision. The proposed module can be easily integrated into end-to-end
network architectures and enjoys performance gains while introducing negligible
computational cost. The learned occlusion mask can be further fed into a
subsequent network cascade with dual feature pyramids with which we achieve
state-of-the-art performance. At the time of submission, our method, called
MaskFlownet, surpasses all published optical flow methods on the MPI Sintel,
KITTI 2012 and 2015 benchmarks. Code is available at
https://github.com/microsoft/MaskFlownet.
