  Superimposed text annotations have been under-investigated, yet are
ubiquitous, useful and important, especially in medical images. Medical images
also highlight the challenges posed by low resolution, noise and superimposed
textual meta-information. Therefor we probed the impact of superimposing text
onto medical images on VQA. Our results revealed that this textual
meta-information can be added without severely degrading key measures of VQA
performance. Our findings are significant because they validate the practice of
superimposing text on images, even for medical images subjected to the VQA task
using AI techniques. The work helps advance understanding of VQA in general
and, in particular, in the domain of healthcare and medicine.
