  Referring expression comprehension aims to locate the object instance
described by a natural language referring expression in an image. This task is
compositional and inherently requires visual reasoning on top of the
relationships among the objects in the image. Meanwhile, the visual reasoning
process is guided by the linguistic structure of the referring expression.
However, existing approaches treat the objects in isolation or only explore the
first-order relationships between objects without being aligned with the
potential complexity of the expression. Thus it is hard for them to adapt to
the grounding of complex referring expressions. In this paper, we explore the
problem of referring expression comprehension from the perspective of
language-driven visual reasoning, and propose a dynamic graph attention network
to perform multi-step reasoning by modeling both the relationships among the
objects in the image and the linguistic structure of the expression. In
particular, we construct a graph for the image with the nodes and edges
corresponding to the objects and their relationships respectively, propose a
differential analyzer to predict a language-guided visual reasoning process,
and perform stepwise reasoning on top of the graph to update the compound
object representation at every node. Experimental results demonstrate that the
proposed method can not only significantly surpass all existing
state-of-the-art algorithms across three common benchmark datasets, but also
generate interpretable visual evidences for stepwisely locating the objects
referred to in complex language descriptions.
