  Capsule networks (CapsNets) have recently shown promise to excel in most
computer vision tasks, especially pertaining to scene understanding. In this
paper, we explore CapsNet's capabilities in optical flow estimation, a task at
which convolutional neural networks (CNNs) have already outperformed other
approaches. We propose a CapsNet-based architecture, termed FlowCaps, which
attempts to a) achieve better correspondence matching via finer-grained,
motion-specific, and more-interpretable encoding crucial for optical flow
estimation, b) perform better-generalizable optical flow estimation, c) utilize
lesser ground truth data, and d) significantly reduce the computational
complexity in achieving good performance, in comparison to its
CNN-counterparts.
