Cross-sample feature interaction enhancement for few-shot fine-grained classification

Few-shot fine-grained image classification faces challenges due to high inter-class feature similarity, significant intra-class variations, and limited data, leading to insufficient model generalization. To address the challenges of category aggregation and separation in few-shot fine-grained scenarios, this paper proposes a classification network enhanced by cross-sample feature interaction. The cross-sample feature interaction enhancement process includes three core components: the Cross-domain Feature Attention Network (CFAN), which enhances feature consistency between support and query sets through channel and spatial attention mechanisms, focusing on capturing critical detail regions; the Global Dependency Augmentation Module (GDAM), which explicitly models dependencies between distant pixels and integrates local and global information; and the Cross-class Interaction Module (CCIM), which uses a bidirectional self-attention mechanism to align local features of query and support samples through complementary and interactive cross-sample features. Additionally, dynamic generation of inter-class feature contrast relationships improves inter-class feature discrimination. Experimental results demonstrate that the proposed method achieves superior performance compared to mainstream methods on multiple public few-shot fine-grained classification datasets, with particularly remarkable results under extremely few-shot conditions. The proposed method significantly improves the accuracy and robustness of few-shot fine-grained classification through an efficient feature interaction mechanism.