  Learning new concepts from a few of samples is a standard challenge in
computer vision. The main directions to improve the learning ability of
few-shot training models include (i) a robust similarity learning and (ii)
generating or hallucinating additional data from the limited existing samples.
In this paper, we follow the latter direction and present a novel data
hallucination model. Currently, most datapoint generators contain a specialized
network (i.e., GAN) tasked with hallucinating new datapoints, thus requiring
large numbers of annotated data for their training in the first place. In this
paper, we propose a novel less-costly hallucination method for few-shot
learning which utilizes saliency maps. To this end, we employ a saliency
network to obtain the foregrounds and backgrounds of available image samples
and feed the resulting maps into a two-stream network to hallucinate datapoints
directly in the feature space from viable foreground-background combinations.
To the best of our knowledge, we are the first to leverage saliency maps for
such a task and we demonstrate their usefulness in hallucinating additional
datapoints for few-shot learning. Our proposed network achieves the state of
the art on publicly available datasets.
