  Human beings can recognize new objects with only a few labeled examples,
however, few-shot learning remains a challenging problem for machine learning
systems. Most previous algorithms in few-shot learning only utilize spatial
information of the images. In this paper, we propose to integrate the frequency
information into the learning model to boost the discrimination ability of the
system. We employ Discrete Cosine Transformation (DCT) to generate the
frequency representation, then, integrate the features from both the spatial
domain and frequency domain for classification. The proposed strategy and its
effectiveness are validated with different backbones, datasets, and algorithms.
Extensive experiments demonstrate that the frequency information is
complementary to the spatial representations in few-shot classification. The
classification accuracy is boosted significantly by integrating features from
both the spatial and frequency domains in different few-shot learning tasks.
