Few-shot image classification has become a popular research topic for its wide application in real-world scenarios, however the problem of supervision collapse induced by single image-level annotation remains a major challenge. Existing methods aim to tackle this problem by locating and aligning relevant local features. However, the high intra-class variability in real-world images poses significant challenges in locating semantically relevant local regions under few-shot settings. Drawing inspiration from the human's complementary learning system, which excels at rapidly capturing and integrating semantic features from limited examples, we propose the generalization-optimized Systems Consolidation Adaptive Memory Dual-Network, SCAM-Net. This approach simulates the systems consolidation of complementary learning system with an adaptive memory module, which successfully addresses the difficulty of identifying meaningful features in few-shot scenarios. Specifically, we construct a Hippocampus-Neocortex dual-network that consolidates structured representation of each category, the structured representation is then stored and adaptively regulated following the generalization optimization principle in a long-term memory inside Neocortex. Extensive experiments on benchmark datasets show that the proposed model has achieved state-of-the-art performance.

翻译：小样本图像分类因其在现实场景中的广泛应用已成为热门研究课题，然而由单一图像级标注引发的监督坍缩问题仍是主要挑战。现有方法试图通过定位和对齐相关局部特征来解决此问题。然而，现实图像中较高的类内差异性给小样本设置下定位语义相关局部区域带来了显著挑战。受人类互补学习系统擅长从有限样本中快速捕获并整合语义特征的启发，我们提出泛化优化的系统巩固自适应记忆双网络——SCAM-Net。该方法通过自适应记忆模块模拟互补学习系统的系统巩固过程，成功解决了小样本场景中识别有意义特征的难题。具体而言，我们构建了海马体-新皮层双网络，用于巩固每个类别的结构化表征，该结构化表征随后依据泛化优化原则在新皮层内部的长时记忆中进行存储与自适应调节。在基准数据集上的大量实验表明，所提模型取得了最先进的性能。