Mutual Correlation Network for few-shot learning

Most metric-based Few-Shot Learning (FSL) methods focus on learning good embeddings of images. However, these methods either lack the ability to explore the cross-correlation (i.e., correlated information) between image pairs or explore limited consensus among the correlation map constrained by the limited receptive field of CNN. We propose a Mutual Correlation Network (MCNet) to explore global consensus among the correlation map by using the self-attention mechanism which has a global receptive field. Our MCNet contains two core modules: (1) a multi-level embedding module that generates multi-level embeddings for an image pair which capture hierarchical semantics, and (2) a mutual correlation module that refines correlation map of two embeddings and generates more robust relational embeddings. Extensive experiments show that our MCNet achieves competitive results on four widely-used few-shot classification benchmarks miniImageNet, tieredImageNet, CUB-200-2011, and CIFAR-FS. Code is available at https://github.com/DRGreat/MCNet.