Making the V in Text-VQA Matter
Shamanthak Hegde Soumya Jahagirdar Shankar Gangisetty
Text-based VQA aims at answering questions by reading the text present in the
images. It requires a large amount of scene-text relationship understanding
compared to the VQA task. Recent studies have shown that the question-answer
pairs in the dataset are more focused on the text present in the image but less
importance is given to visual features and some questions do not require
understanding the image. The models trained on this dataset predict biased
answers due to the lack of understanding of visual context. For example, in
questions like "What is written on the signboard?", the answer predicted by the
model is always "STOP" which makes the model to ignore the image. To address
these issues, we propose a method to learn visual features (making V matter in
TextVQA) along with the OCR features and question features using VQA dataset as
external knowledge for Text-based VQA. Specifically, we combine the TextVQA
dataset and VQA dataset and train the model on this combined dataset. Such a
simple, yet effective approach increases the understanding and correlation
between the image features and text present in the image, which helps in the
better answering of questions. We further test the model on different datasets
and compare their qualitative and quantitative results.