  We propose Attention Grounder (AttnGrounder), a single-stage end-to-end
trainable model for the task of visual grounding. Visual grounding aims to
localize a specific object in an image based on a given natural language text
query. Unlike previous methods that use the same text representation for every
image region, we use a visual-text attention module that relates each word in
the given query with every region in the corresponding image for constructing a
region dependent text representation. Furthermore, for improving the
localization ability of our model, we use our visual-text attention module to
generate an attention mask around the referred object. The attention mask is
trained as an auxiliary task using a rectangular mask generated with the
provided ground-truth coordinates. We evaluate AttnGrounder on the Talk2Car
dataset and show an improvement of 3.26% over the existing methods.
