Hybrid Feature Collaborative Reconstruction Network for Few-Shot Fine-Grained Image Classification

Abstract

Our research focuses on few-shot fine-grained image classification (FS-FGIC), which faces two main challenges: the similarity of fine-grained objects and a limited number of samples. Traditional feature reconstruction networks enhance key features through spatial reconstruction and error minimization but often fail to capture interclass differences with limited samples. We propose a Hybrid Feature Collaborative Reconstruction Network (HFCR-Net) with two key components: the Hybrid Feature Fusion Process (HFFP) and the Hybrid Feature Reconstruction Process (HFRP). In HFRP, dynamic weight adjustment is employed to enhance spatial dependencies and channel correlations, increasing inter-class differences. In HFRP, we introduce channel dimension reconstruction to improve the processes of support-to-query and query-to-support reconstruction, further increasing interclass and reducing intra-class differences. Extensive experiments on three widely used fine-grained datasets confirm the effectiveness and superiority of our approach.