Global and local representation collaborative learning for few-shot learning

This paper proposes a new framework called global and local representation collaborative learning (GLCL), which combines the complementary advantages of global equivariance and local aggregation and designs a cross-view contrastive learning to promote the consistent learning and implicit exploration of useful knowledge from one another.