Taking an image and question as the input of our method, it can output the
text-based answer of the query question about the given image, so called Visual
Question Answering (VQA). There are two main modules in our algorithm. Given a
natural language question about an image, the first module takes the question
as input and then outputs the basic questions of the main given question. The
second module takes the main question, image and these basic questions as input
and then outputs the text-based answer of the main question. We formulate the
basic questions generation problem as a LASSO optimization problem, and also
propose a criterion about how to exploit these basic questions to help answer
main question. Our method is evaluated on the challenging VQA dataset and
yields state-of-the-art accuracy, 60.34% in open-ended task.

本研究提出了一种针对图像和问题的视觉问答算法，包含两个主要模块：第一个模块将问题作为输入，输出主要给定问题的基础问题；第二个模块将问题和图像作为输入，输出问题的基于文本的答案。研究将基础问题生成问题视为 LASSO 优化问题，并提出一种标准来利用这些基础问题帮助回答主要问题。经过在具有挑战性的 VQA 数据集上的验证，本研究的方法实现了最先进的准确性，在开放式任务中达到了 60.34％的准确度。