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Previous studies such as VizWiz find that Visual Question Answering (VQA)systems that can read and reason about text in images are useful in applicationareas such as assisting visually-impaired people. TextVQA is a VQA datasetgeared towards this problem, where the questions require answering systems toread and reason about visual objects and text objects in images. One keychallenge in TextVQA is the design of a system that effectively reasons notonly about visual and text objects individually, but also about the spatialrelationships between these objects. This motivates the use of 'edge features',that is, information about the relationship between each pair of objects. Somecurrent TextVQA models address this problem but either only use categories ofrelations (rather than edge feature vectors) or do not use edge features withinthe Transformer architectures. In order to overcome these shortcomings, wepropose a Graph Relation Transformer (GRT), which uses edge information inaddition to node information for graph attention computation in theTransformer. We find that, without using any other optimizations, the proposedGRT method outperforms the accuracy of the M4C baseline model by 0.65% on theval set and 0.57% on the test set. Qualitatively, we observe that the GRT hassuperior spatial reasoning ability to M4C.

Graph Relation Transformer: Incorporating pairwise object features into the Transformer architecture