  Referring Expression Comprehension (REC) is an emerging research spot in
computer vision, which refers to detecting the target region in an image given
an text description. Most existing REC methods follow a multi-stage pipeline,
which are computationally expensive and greatly limit the application of REC.
In this paper, we propose a one-stage model towards real-time REC, termed
Real-time Global Inference Network (RealGIN). RealGIN addresses the diversity
and complexity issues in REC with two innovative designs: the Adaptive Feature
Selection (AFS) and the Global Attentive ReAsoNing unit (GARAN). AFS adaptively
fuses features at different semantic levels to handle the varying content of
expressions. GARAN uses the textual feature as a pivot to collect
expression-related visual information from all regions, and thenselectively
diffuse such information back to all regions, which provides sufficient context
for modeling the complex linguistic conditions in expressions. On five
benchmark datasets, i.e., RefCOCO, RefCOCO+, RefCOCOg, ReferIt and Flickr30k,
the proposed RealGIN outperforms most prior works and achieves very competitive
performances against the most advanced method, i.e., MAttNet. Most importantly,
under the same hardware, RealGIN can boost the processing speed by about 10
times over the existing methods.
