Adaptive Feature Representation Based On Contrastive Learning For Few-Shot Classification

Few-shot image classification is a challenging task aim to classific unseen images in scenarios with limited samples. Recent work demonstrate that local discriminative features have better representational capabilities than global features. In this paper, we propose a novel method to extract local discriminative features from images and calculate better class representations through contrastive learning. We divide the training paradigm into two stages, referred to as pre-training and meta-training. In the pre-training stage, we employ a global contrastive loss and introduce an improved Mutual Maximum Local Matching Contrastive Loss (MMCL) to obtain local discriminative features while disregarding irrelevant background features. During the meta-training stage, we introduce a new contrastive learning-based Adaptive Class Representation Computation Network (ACRC-Net) to enhance the generalization capability of class representations. It has the ability to adaptively compute class representations, specifically by comparing the similarity between features of the same and different classes within the same tasks to obtain the optimal subset of class representations. Based on the optimal subset of representations, we obtain the final class representation. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on general datasets.