[D] MANN paper: why no convolutions?

In the 2016 paper about memory augmented neural networks, https://arxiv.org/abs/1605.06065 , the main results are presented using the Omniglot dataset. However, the model in that paper apparently doesn't use any convolutions: "The image is then flattened into a vector, concatenated with a randomly chosen, episode-specific label, and fed as input to the network controller."

To me it seems clear that a CNN can process such images much better. Just compare the performance of a fully connected network to that of a CNN on MNIST, for example. Does anybody know, or have an idea, why didn't they add a convolutional layer or two to preprocess the image before feeding it into the controller?