Metric Learning for visual similarity has mostly adopted binary supervision
indicating whether a pair of images are of the same class or not. Such a binary
indicator covers only a limited subset of image relations, and is not
sufficient to represent semantic similarity between images described by
continuous and/or structured labels such as object poses, image captions, and
scene graphs. Motivated by this, we present a novel method for deep metric
learning using continuous labels. First, we propose a new triplet loss that
allows distance ratios in the label space to be preserved in the learned metric
space. The proposed loss thus enables our model to learn the degree of
similarity rather than just the order. Furthermore, we design a triplet mining
strategy adapted to metric learning with continuous labels. We address three
different image retrieval tasks with continuous labels in terms of human poses,
room layouts and image captions, and demonstrate the superior performance of
our approach compared to previous methods.

本文引入了一种基于连续标签的深层度量学习新方法，包括使用连续标签进行三元组损失和适应连续标签的三元组挖掘策略，同时实验结果显示该方法在三种不同的图像检索任务中表现出较优异性能。