  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.
