  We address the problem of grounding free-form textual phrases by using weak
supervision from image-caption pairs. We propose a novel end-to-end model that
uses caption-to-image retrieval as a `downstream' task to guide the process of
phrase localization. Our method, as a first step, infers the latent
correspondences between regions-of-interest (RoIs) and phrases in the caption
and creates a discriminative image representation using these matched RoIs. In
a subsequent step, this (learned) representation is aligned with the caption.
Our key contribution lies in building this `caption-conditioned' image encoding
which tightly couples both the tasks and allows the weak supervision to
effectively guide visual grounding. We provide an extensive empirical and
qualitative analysis to investigate the different components of our proposed
model and compare it with competitive baselines. For phrase localization, we
report an improvement of 4.9% (absolute) over the prior state-of-the-art on the
VisualGenome dataset. We also report results that are at par with the
state-of-the-art on the downstream caption-to-image retrieval task on COCO and
Flickr30k datasets.
