  Visual question answering (VQA) demands simultaneous comprehension of both
the image visual content and natural language questions. In some cases, the
reasoning needs the help of common sense or general knowledge which usually
appear in the form of text. Current methods jointly embed both the visual
information and the textual feature into the same space. However, how to model
the complex interactions between the two different modalities is not an easy
task. In contrast to struggling on multimodal feature fusion, in this paper, we
propose to unify all the input information by natural language so as to convert
VQA into a machine reading comprehension problem. With this transformation, our
method not only can tackle VQA datasets that focus on observation based
questions, but can also be naturally extended to handle knowledge-based VQA
which requires to explore large-scale external knowledge base. It is a step
towards being able to exploit large volumes of text and natural language
processing techniques to address VQA problem. Two types of models are proposed
to deal with open-ended VQA and multiple-choice VQA respectively. We evaluate
our models on three VQA benchmarks. The comparable performance with the
state-of-the-art demonstrates the effectiveness of the proposed method.
