  Text-VQA aims at answering questions that require understanding the textual
cues in an image. Despite the great progress of existing Text-VQA methods,
their performance suffers from insufficient human-labeled question-answer (QA)
pairs. However, we observe that, in general, the scene text is not fully
exploited in the existing datasets -- only a small portion of the text in each
image participates in the annotated QA activities. This results in a huge waste
of useful information. To address this deficiency, we develop a new method to
generate high-quality and diverse QA pairs by explicitly utilizing the existing
rich text available in the scene context of each image. Specifically, we
propose, TAG, a text-aware visual question-answer generation architecture that
learns to produce meaningful, and accurate QA samples using a multimodal
transformer. The architecture exploits underexplored scene text information and
enhances scene understanding of Text-VQA models by combining the generated QA
pairs with the initial training data. Extensive experimental results on two
well-known Text-VQA benchmarks (TextVQA and ST-VQA) demonstrate that our
proposed TAG effectively enlarges the training data that helps improve the
Text-VQA performance without extra labeling effort. Moreover, our model
outperforms state-of-the-art approaches that are pre-trained with extra
large-scale data. Code is available at https://github.com/HenryJunW/TAG.
