First, we use the simple MLP baseline model used in Kafle and Kanan (2016) to assess the two data augmentation methods. The MLP model treats VQA as a classification problem with concatenated image and question features given to the model as features and answers as categories. CNN features from ResNet-152 and the skip-thought vectors<cite> (Kiros et al., 2015)</cite> are used as image and question features respectively.