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TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation
The code base for TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation <br>Jun Wang, Mingfei Gao, Yuqian Hu, Ramprasaath R. Selvaraju, Chetan Ramaiah, Ran Xu, Joseph F. JaJa, Larry S. Davis
NEWS
- [22-09-30] 🔥 TAG is accepted at BMVC 2022.
Abstract
<div style="text-align: justify">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 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. </div> <!-- <p> </p> --><br>
Features
- A generic text-aware question-answer generation approach for Text-related VQA.
- Support major Text-VQA datasets: TextVQA, ST-VQA.
Installation
See installation instructions.
Getting Started
Model Zoo and Baselines
We provide a large set of trained models available for download in the TAG Model Zoo.
Citation
Please cite our work if you found it useful,
@inproceedings{Wang_2022_BMVC,
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}
}