  Despite the significant progress that has been made on estimating optical
flow recently, most estimation methods, including classical and deep learning
approaches, still have difficulty with multi-scale estimation, real-time
computation, and/or occlusion reasoning. In this paper, we introduce dilated
convolution and occlusion reasoning into unsupervised optical flow estimation
to address these issues. The dilated convolution allows our network to avoid
upsampling via deconvolution and the resulting gridding artifacts. Dilated
convolution also results in a smaller memory footprint which speeds up
interference. The occlusion reasoning prevents our network from learning
incorrect deformations due to occluded image regions during training. Our
proposed method outperforms state-of-the-art unsupervised approaches on the
KITTI benchmark. We also demonstrate its generalization capability by applying
it to action recognition in video.
