SFRS.md

Self-supervising Fine-grained Region Similarities (ECCV'20 Spotlight)

NetVLAD first proposed a VLAD layer trained with triplet loss, and then SARE introduced two softmax-based losses (sare_ind and sare_joint) to boost the training. Our SFRS is trained in generations with self-enhanced soft-label losses to achieve state-of-the-art performance.