  Visual Question Answering (VQA) is challenging due to the complex cross-modal
relations. It has received extensive attention from the research community.
From the human perspective, to answer a visual question, one needs to read the
question and then refer to the image to generate an answer. This answer will
then be checked against the question and image again for the final
confirmation. In this paper, we mimic this process and propose a fully
attention based VQA architecture. Moreover, an answer-checking module is
proposed to perform a unified attention on the jointly answer, question and
image representation to update the answer. This mimics the human answer
checking process to consider the answer in the context. With answer-checking
modules and transferred BERT layers, our model achieves the state-of-the-art
accuracy 71.57\% using fewer parameters on VQA-v2.0 test-standard split.
