关键词: 小样本学习, 图像分类, 度量学习, 原型网络, 混合注意力机制

Abstract: In few-shot learning tasks for novel class recognition under scarce data, prototype networks have gained significant attention. However, traditional methods relying on global averaging for prototype computation often suffer from background interference in extremely limited samples and insufficient coverage of local features. To address these issues, this paper proposes an enhanced prototype generation approach by integrating local feature representations and a hybrid attention mechanism. First, we extract feature maps of samples through a backbone network and decompose them into local descriptors. Element-wise mean aggregation is then applied to these descriptors, generating prototypes that fuse multi-scale details, thereby preserving global information while capturing rich contextual relationships and improving prototype discriminability and robustness. Second, a hybrid channel-spatial attention mechanism is designed: the dual-pooling channel attention module employs global average pooling and max-pooling pathways to dynamically learn channel-wise weights for suppressing background noise; the enhanced spatial attention module evaluates regional relevance through a local feature similarity matrix, combined with channel statistics derived from global average and max-pooling, to collaboratively generate foreground-targeted spatial attention distributions. The two modules focus on critical local regions of images in complementary ways, enhancing prototype representativeness. Compared with existing methods, our approach demonstrates superior performance in classification tasks across varying granularities. For coarse-grained classification, accuracy improvements of 5.60% and 6.02% are achieved over baseline models under two experimental settings. In fine-grained tasks, the proposed method outperforms state-of-the-art methods by an average margin of 5.42%.

Key words: few-shot learning, image classification, metric learning, prototype networks, hybrid attention mechanism