  Different approaches have been proposed to Visual Question Answering (VQA).
However, few works are aware of the behaviors of varying joint modality methods
over question type prior knowledge extracted from data in constraining answer
search space, of which information gives a reliable cue to reason about answers
for questions asked in input images. In this paper, we propose a novel VQA
model that utilizes the question-type prior information to improve VQA by
leveraging the multiple interactions between different joint modality methods
based on their behaviors in answering questions from different types. The solid
experiments on two benchmark datasets, i.e., VQA 2.0 and TDIUC, indicate that
the proposed method yields the best performance with the most competitive
approaches.
