  State-of-the-art neural network models estimate large displacement optical
flow in multi-resolution and use warping to propagate the estimation between
two resolutions. Despite their impressive results, it is known that there are
two problems with the approach. First, the multi-resolution estimation of
optical flow fails in situations where small objects move fast. Second, warping
creates artifacts when occlusion or dis-occlusion happens. In this paper, we
propose a new neural network module, Deformable Cost Volume, which alleviates
the two problems. Based on this module, we designed the Deformable Volume
Network (Devon) which can estimate multi-scale optical flow in a single high
resolution. Experiments show Devon is more suitable in handling small objects
moving fast and achieves comparable results to the state-of-the-art methods in
public benchmarks.
