Unsupervised Learning of Optical Flow With Patch Consistency and Occlusion Estimation

•A patch-based census constancy loss is proposed by patch-based warping to further improve the performance of unsupervised optical flow estimation.•A parallel decoder branch is devised for occlusion mask learning in an unsupervised way.•The pseudo label generated from the forward-backward consistency check is used to derive a mask loss for occlusion mask learning.•Our method achieves the-state-of-the-art results among the unsupervised learning methods that are using the FlowNet-liked network structure.