  Localizing natural language phrases in images is a challenging problem that
requires joint understanding of both the textual and visual modalities. In the
unsupervised setting, lack of supervisory signals exacerbate this difficulty.
In this paper, we propose a novel framework for unsupervised visual grounding
which uses concept learning as a proxy task to obtain self-supervision. The
simple intuition behind this idea is to encourage the model to localize to
regions which can explain some semantic property in the data, in our case, the
property being the presence of a concept in a set of images. We present
thorough quantitative and qualitative experiments to demonstrate the efficacy
of our approach and show a 5.6% improvement over the current state of the art
on Visual Genome dataset, a 5.8% improvement on the ReferItGame dataset and
comparable to state-of-art performance on the Flickr30k dataset.
