  Visual grounding is a ubiquitous building block in many vision-language tasks
and yet remains challenging due to large variations in visual and linguistic
features of grounding entities, strong context effect and the resulting
semantic ambiguities. Prior works typically focus on learning representations
of individual phrases with limited context information. To address their
limitations, this paper proposes a language-guided graph representation to
capture the global context of grounding entities and their relations, and
develop a cross-modal graph matching strategy for the multiple-phrase visual
grounding task. In particular, we introduce a modular graph neural network to
compute context-aware representations of phrases and object proposals
respectively via message propagation, followed by a graph-based matching module
to generate globally consistent localization of grounding phrases. We train the
entire graph neural network jointly in a two-stage strategy and evaluate it on
the Flickr30K Entities benchmark. Extensive experiments show that our method
outperforms the prior state of the arts by a sizable margin, evidencing the
efficacy of our grounding framework. Code is available at
"https://github.com/youngfly11/LCMCG-PyTorch".
