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.