  Unsupervised object discovery aims to localize objects in images, while
removing the dependence on annotations required by most deep learning-based
methods. To address this problem, we propose a fully unsupervised, bottom-up
approach, for multiple objects discovery. The proposed approach is a two-stage
framework. First, instances of object parts are segmented by using the
intra-image similarity between self-supervised local features. The second step
merges and filters the object parts to form complete object instances. The
latter is performed by two CNN models that capture semantic information on
objects from the entire dataset. We demonstrate that the pseudo-labels
generated by our method provide a better precision-recall trade-off than
existing single and multiple objects discovery methods. In particular, we
provide state-of-the-art results for both unsupervised class-agnostic object
detection and unsupervised image segmentation.
