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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-toimage 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 the subsequent step, this learned representation is aligned with the caption. Our key contribution lies in building this "captionconditioned" image encoding which tightly couples both the tasks and allows the weak supervision to effectively guide visual grounding. We provide 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 improvements of 4.9% and 1.3% (absolute) over prior state-of-the-art on the VisualGenome and Flickr30k Entities datasets. 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.Recent works [20,21] have shown evidence that operating under such a paradigm helps boost performance for imagecaption matching. Generally, these models consist of two stages: (1) a local matching module that infers the latent region-phrase correspondences to generate local matching information, and (2) a global matching module that uses this information to perform image-caption matching. This setup allows phrase grounding to act as an intermediate and a prerequisite task for image-caption matching. It is important to note that the primary objective of such works has been on image-caption matching and not phrase grounding.An artifact of training under such a paradigm is the amplification of correlations between selective regions and phrases."Young girl holding a kitten" by Gennadiy Kolodkin is licensed under CC BY-NC-ND 2.0.

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