  Visual grounding aims to localize an object in an image referred to by a
textual query phrase. Various visual grounding approaches have been proposed,
and the problem can be modularized into a general framework: proposal
generation, multi-modal feature representation, and proposal ranking. Of these
three modules, most existing approaches focus on the latter two, with the
importance of proposal generation generally neglected. In this paper, we
rethink the problem of what properties make a good proposal generator. We
introduce the diversity and discrimination simultaneously when generating
proposals, and in doing so propose Diversified and Discriminative Proposal
Networks model (DDPN). Based on the proposals generated by DDPN, we propose a
high performance baseline model for visual grounding and evaluate it on four
benchmark datasets. Experimental results demonstrate that our model delivers
significant improvements on all the tested data-sets (e.g., 18.8\% improvement
on ReferItGame and 8.2\% improvement on Flickr30k Entities over the existing
state-of-the-arts respectively)
