text: 258,83,1405565545693560840,1938536035,Huy V. Vo,"Our new paper ""Large-Scale Unsupervised Object Discovery"" is on arxiv: <LINK>. We propose to use ranking methods for object discovery and show that our approach scales better than the baselines while yielding state-of-the-art results on COCO ... <LINK> ... and the large OpenImages dataset with 1.7M images.",https://arxiv.org/abs/2106.06650,"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 this https URL ",Large-Scale Unsupervised Object Discovery,2,"['Our new paper ""Large-Scale Unsupervised Object Discovery"" is on arxiv: <LINK>. We propose to use ranking methods for object discovery and show that our approach scales better than the baselines while yielding state-of-the-art results on COCO ... <LINK>', '... and the large OpenImages dataset with 1.7M images.']",21,06,307