Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion

Motion, measured via optical flow, provides a powerful cue to discover and learn objects in images and videos. However, compared to using appearance, it has some blind spots, such as the fact that objects become invisible if they do not move. In this work, we propose an approach that combines the strengths of motion-based and appearance-based segmentation. We propose to supervise an image segmentation network, tasking it with predicting regions that are likely to contain simple motion patterns, and thus likely to correspond to objects. We apply this network in two modes. In the unsupervised video segmentation mode, the network is trained on a collection of unlabelled videos, using the learning process itself as an algorithm to segment these videos. In the unsupervised image segmentation model, the network is learned using videos and applied to segment independent still images. With this, we obtain strong empirical results in unsupervised video and image segmentation, significantly outperforming the state of the art on benchmarks such as DAVIS, sometimes with a $5\%$ IoU gap.