  In this paper we address the problem of unsupervised localization of objects
in single images. Compared to previous state-of-the-art method our method is
fully unsupervised in the sense that there is no prior instance level or
category level information about the image. Furthermore, we treat each image
individually and do not rely on any neighboring image similarity. We employ
deep-learning based generation of saliency maps and region proposals to tackle
this problem. First salient regions in the image are determined using an
encoder/decoder architecture. The resulting saliency map is matched with region
proposals from a class agnostic region proposal network to roughly localize the
candidate object regions. These regions are further refined based on the
overlap and similarity ratios. Our experimental evaluations on a benchmark
dataset show that the method gets close to current state-of-the-art methods in
terms of localization accuracy even though these make use of multiple frames.
Furthermore, we created a more challenging and realistic dataset with multiple
object categories and varying viewpoint and illumination conditions for
evaluating the method's performance in real world scenarios.
