  We propose a new pipeline for optical flow computation, based on Deep
Learning techniques. We suggest using a Siamese CNN to independently, and in
parallel, compute the descriptors of both images. The learned descriptors are
then compared efficiently using the L2 norm and do not require network
processing of patch pairs. The success of the method is based on an innovative
loss function that computes higher moments of the loss distributions for each
training batch. Combined with an Approximate Nearest Neighbor patch matching
method and a flow interpolation technique, state of the art performance is
obtained on the most challenging and competitive optical flow benchmarks.
