  Previous studies such as VizWiz find that Visual Question Answering (VQA)
systems that can read and reason about text in images are useful in application
areas such as assisting visually-impaired people. TextVQA is a VQA dataset
geared towards this problem, where the questions require answering systems to
read and reason about visual objects and text objects in images. One key
challenge in TextVQA is the design of a system that effectively reasons not
only about visual and text objects individually, but also about the spatial
relationships between these objects. This motivates the use of 'edge features',
that is, information about the relationship between each pair of objects. Some
current TextVQA models address this problem but either only use categories of
relations (rather than edge feature vectors) or do not use edge features within
the Transformer architectures. In order to overcome these shortcomings, we
propose a Graph Relation Transformer (GRT), which uses edge information in
addition to node information for graph attention computation in the
Transformer. We find that, without using any other optimizations, the proposed
GRT method outperforms the accuracy of the M4C baseline model by 0.65% on the
val set and 0.57% on the test set. Qualitatively, we observe that the GRT has
superior spatial reasoning ability to M4C.
