Few-shot learning (FSL) addresses learning tasks in which only few samples are available for selected object categories. In this paper, we propose a deep learning framework for data hallucination, which overcomes the above limitation and alleviates possible overfitting problems. In particular, our method exploits semantic information into the hallucination process, and thus the augmented data would be able to exhibit semantics-oriented modes of variation for improved FSL performances. Very promising performances on CIFAR-100 and AwA datasets confirm the effectiveness of our proposed method for FSL.