TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation

Jun Wang (University of Maryland, College Park),* Mingfei Gao (Apple), Yuqian Hu (University of Maryland, College Park), Ramprasaath R. Selvaraju (Salesforce Research), Chetan Ramaiah (Google), Ran Xu (Salesforce Research), Joseph JaJa (University of Maryland, College Park), Larry Davis (University of Maryland)
The 33rd British Machine Vision Conference


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. \href{https://github.com/HenryJunW/TAG}{Code is available here}.



author    = {Jun Wang and Mingfei Gao and Yuqian Hu and Ramprasaath R. Selvaraju and Chetan Ramaiah and Ran Xu and Joseph JaJa and Larry Davis},
title     = {TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year      = {2022},
url       = {https://bmvc2022.mpi-inf.mpg.de/0033.pdf}

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