Contrastive prototype loss based discriminative feature network for few-shot learning

Metric-based few-shot image classification methods generally perform classification by comparing the distances between the query sample features and the prototypes of each class. These methods often focus on constructing prototype representations for each class or learning a metric, while neglecting the significance of the feature space itself. In this paper, we redirect the focus to feature space construction, with the goal of constructing a discriminative feature space for few-shot image classification tasks. To this end, we designed a contrastive prototype loss that incorporates the distribution of query samples with respect to class prototypes in the feature space, emphasizing intra-class compactness and inter-class separability, thereby guiding the model to learn a more discriminative feature space. Based on this loss, we propose a contrastive prototype loss based discriminative feature network (CPL-DFNet) to address few-shot image classification tasks. CPL-DFNet enhances sample utilization by fully leveraging the distance relationships between query samples and class prototypes in the feature space, creating more favorable conditions for few-shot image classification tasks and significantly improving classification performance. We conducted extensive experiments on both general and fine-grained few-shot image classification benchmark datasets to validate the effectiveness of the proposed CPL-DFNet method. The experimental results show that CPL-DFNet can effectively perform few-shot image classification tasks and outperforms many existing methods across various task scenarios, demonstrating significant performance advantages.