  We propose a new self-supervised approach to image feature learning from
motion cue. This new approach leverages recent advances in deep learning in two
directions: 1) the success of training deep neural network in estimating
optical flow in real data using synthetic flow data; and 2) emerging work in
learning image features from motion cues, such as optical flow. Building on
these, we demonstrate that image features can be learned in self-supervision by
first training an optical flow estimator with synthetic flow data, and then
learning image features from the estimated flows in real motion data. We
demonstrate and evaluate this approach on an image segmentation task. Using the
learned image feature representation, the network performs significantly better
than the ones trained from scratch in few-shot segmentation tasks.
