  Image recognition is an essential baseline for deep metric learning.
Hierarchical knowledge about image classes depicts inter-class similarities or
dissimilarities. Effective fusion of hierarchical knowledge about image classes
to enhance image recognition remains a challenging topic to advance. In this
paper, we propose a novel deep metric learning based method to effectively fuse
hierarchical prior knowledge about image classes and enhance image recognition
performances in an end-to-end supervised regression manner. Existing deep
metric learning incorporated image classification mainly exploits qualitative
relativity between image classes, i.e., whether sampled images are from the
same class. A new triplet loss function term that exploits quantitative
relativity and aligns distances in model latent space with those in knowledge
space is also proposed and incorporated in the proposed dual-modality fusion
method. Experimental results indicate that the proposed method enhanced image
recognition performances and outperformed baseline and existing methods on
CIFAR-10, CIFAR-100, Mini-ImageNet, and ImageNet-1K datasets.
