We present Multiple-Question Multiple-Answer (MQMA), a novel approach to do
text-VQA in encoder-decoder transformer models. The text-VQA task requires a
model to answer a question by understanding multi-modal content: text
(typically from OCR) and an associated image. To the best of our knowledge,
almost all previous approaches for text-VQA process a single question and its
associated content to predict a single answer. In order to answer multiple
questions from the same image, each question and content are fed into the model
multiple times. In contrast, our proposed MQMA approach takes multiple
questions and content as input at the encoder and predicts multiple answers at
the decoder in an auto-regressive manner at the same time. We make several
novel architectural modifications to standard encoder-decoder transformers to
support MQMA. We also propose a novel MQMA denoising pre-training task which is
designed to teach the model to align and delineate multiple questions and
content with associated answers. MQMA pre-trained model achieves
state-of-the-art results on multiple text-VQA datasets, each with strong
baselines. Specifically, on OCR-VQA (+2.5%), TextVQA (+1.4%), ST-VQA (+0.6%),
DocVQA (+1.1%) absolute improvements over the previous state-of-the-art
approaches.

通过多问多答（MQMA）方法，在编码 - 解码变压器模型中进行文本 - VQA（Visual Question Answering）。通过多次将不同问题和内容输入到模型中进行预测，实现对同一图像的多问题回答预测。提出几个新颖的架构修改来支持 MQMA，并且通过 MQMA 去噪的预训练任务，使模型能够对多个问题以及相关答案进行对齐和划分。在多个文本 - VQA 数据集上，MQMA 预训练模型实现了与先前最先进方法相比的明显改进（OCR-VQA：+2.5％，TextVQA：+1.4％，ST-VQA：+0.6％，DocVQA：+1.1％）。