  State-of-the-art deep learning algorithms yield remarkable results in many
visual recognition tasks. However, they still fail to provide satisfactory
results in scarce data regimes. To a certain extent this lack of data can be
compensated by multimodal information. Missing information in one modality of a
single data point (e.g. an image) can be made up for in another modality (e.g.
a textual description). Therefore, we design a few-shot learning task that is
multimodal during training (i.e. image and text) and single-modal during test
time (i.e. image). In this regard, we propose a self-paced class-discriminative
generative adversarial network incorporating multimodality in the context of
few-shot learning. The proposed approach builds upon the idea of cross-modal
data generation in order to alleviate the data sparsity problem. We improve
few-shot learning accuracies on the finegrained CUB and Oxford-102 datasets.
