FLCL: Feature-Level Contrastive Learning for Few-Shot Image Classification

Abstract

Few-shot classification is the task of recognizing unseen classes using a limited number of samples. In this paper, we propose a new contrastive learning method called Feature-Level Contrastive Learning (FLCL). FLCL conducts contrastive learning at the feature level and leverages the subtle relationships between positive and negative samples to achieve more effective classification. Additionally, we address the challenges of requiring a large number of negative samples and the difficulty of selecting high-quality negative samples in traditional contrastive learning methods. For feature learning, we design a Feature Enhancement Coding (FEC) module to analyze the interactions and correlations between nonlinear features, enhancing the quality of feature representations. In the metric stage, we propose a centered hypersphere projection metric to map feature vectors onto the hypersphere, improving the comparison between the support and query sets. Experimental results on four few-shot classification benchmark datasets demonstrate that our method, while simple in design, outperforms previous methods and achieves state-of-the-art performance. A detailed ablation study further confirms the effectiveness of each component of our model.