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Different approaches have been proposed to Visual Question Answering (VQA).However, few works are aware of the behaviors of varying joint modality methodsover question type prior knowledge extracted from data in constraining answersearch space, of which information gives a reliable cue to reason about answersfor questions asked in input images. In this paper, we propose a novel VQAmodel that utilizes the question-type prior information to improve VQA byleveraging the multiple interactions between different joint modality methodsbased on their behaviors in answering questions from different types. The solidexperiments on two benchmark datasets, i.e., VQA 2.0 and TDIUC, indicate thatthe proposed method yields the best performance with the most competitiveapproaches.

Multiple interaction learning with question-type prior knowledge for constraining answer search space in visual question answering