Data augmentation for visual question answering

Abstract

Data augmentation is widely used to train deep neural networks for image classification tasks. Simply flipping images can help learning by increasing the number of training images by a factor of two. However, data augmentation in natural language processing is much less studied. Here, we describe two methods for data augmentation for Visual Question Answering (VQA). The first uses existing semantic annotations to generate new questions. The second method is a generative approach using recurrent neural networks. Experiments show the proposed schemes improve performance of baseline and state-of-the-art VQA algorithms.