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Visual Question Answering (VQA) has achieved great success thanks to the fast development of deep neural networks (DNN). On the other hand, the data augmentation, as one of the major tricks for DNN, has been widely used in many computer vision tasks. However, there are few works studying the data augmentation problem for VQA and none of the existing image based augmentation schemes (such as rotation and flipping) can be directly applied to VQA due to its semantic structure – an $$\langle image, question, answer\rangle $$ ⟨ i m a g e , q u e s t i o n , a n s w e r ⟩ triplet needs to be maintained correctly. For …