  A two-stage training paradigm consisting of sequential pre-training and
meta-training stages has been widely used in current few-shot learning (FSL)
research. Many of these methods use self-supervised learning and contrastive
learning to achieve new state-of-the-art results. However, the potential of
contrastive learning in both stages of FSL training paradigm is still not fully
exploited. In this paper, we propose a novel contrastive learning-based
framework that seamlessly integrates contrastive learning into both stages to
improve the performance of few-shot classification. In the pre-training stage,
we propose a self-supervised contrastive loss in the forms of feature vector
vs. feature map and feature map vs. feature map, which uses global and local
information to learn good initial representations. In the meta-training stage,
we propose a cross-view episodic training mechanism to perform the nearest
centroid classification on two different views of the same episode and adopt a
distance-scaled contrastive loss based on them. These two strategies force the
model to overcome the bias between views and promote the transferability of
representations. Extensive experiments on three benchmark datasets demonstrate
that our method achieves competitive results.
