  The promise of self-supervised learning (SSL) is to leverage large amounts of
unlabeled data to solve complex tasks. While there has been excellent progress
with simple, image-level learning, recent methods have shown the advantage of
including knowledge of image structure. However, by introducing hand-crafted
image segmentations to define regions of interest, or specialized augmentation
strategies, these methods sacrifice the simplicity and generality that makes
SSL so powerful. Instead, we propose a self-supervised learning paradigm that
discovers this image structure by itself. Our method, Odin, couples object
discovery and representation networks to discover meaningful image
segmentations without any supervision. The resulting learning paradigm is
simpler, less brittle, and more general, and achieves state-of-the-art transfer
learning results for object detection and instance segmentation on COCO, and
semantic segmentation on PASCAL and Cityscapes, while strongly surpassing
supervised pre-training for video segmentation on DAVIS.
