  Existing approaches to unsupervised object discovery (UOD) do not scale up to
large datasets without approximations that compromise their performance. We
propose a novel formulation of UOD as a ranking problem, amenable to the
arsenal of distributed methods available for eigenvalue problems and link
analysis. Through the use of self-supervised features, we also demonstrate the
first effective fully unsupervised pipeline for UOD. Extensive experiments on
COCO and OpenImages show that, in the single-object discovery setting where a
single prominent object is sought in each image, the proposed LOD (Large-scale
Object Discovery) approach is on par with, or better than the state of the art
for medium-scale datasets (up to 120K images), and over 37% better than the
only other algorithms capable of scaling up to 1.7M images. In the multi-object
discovery setting where multiple objects are sought in each image, the proposed
LOD is over 14% better in average precision (AP) than all other methods for
datasets ranging from 20K to 1.7M images. Using self-supervised features, we
also show that the proposed method obtains state-of-the-art UOD performance on
OpenImages. Our code is publicly available at https://github.com/huyvvo/LOD.
