  We propose a new spatial memory module and a spatial reasoner for the Visual
Grounding (VG) task. The goal of this task is to find a certain object in an
image based on a given textual query. Our work focuses on integrating the
regions of a Region Proposal Network (RPN) into a new multi-step reasoning
model which we have named a Multimodal Spatial Region Reasoner (MSRR). The
introduced model uses the object regions from an RPN as initialization of a 2D
spatial memory and then implements a multi-step reasoning process scoring each
region according to the query, hence why we call it a multimodal reasoner. We
evaluate this new model on challenging datasets and our experiments show that
our model that jointly reasons over the object regions of the image and words
of the query largely improves accuracy compared to current state-of-the-art
models.
