In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function. Compared to the state-of-the-art Deep Metric Learning losses, this function takes advantage of the ranked ordering of the labels in regression problems. To this end, we first show that the loss minimises when datapoints of different labels are ranked and laid at uniform angles between each other in the embedding space. Then, to measure its performance, we apply the proposed loss on a regression task of people counting with a short-range radar in a challenging scenario, namely a vehicle cabin. The introduced approach improves the accuracy as well as the neighboring labels accuracy up to 83.0% and 99.9%: An increase of 6.7%and 2.1% on state-of-the-art methods, respectively.
翻译:在本文中, 我们引入了Label- Aware 排名损失, 这是一种新颖的衡量损失功能。 与最先进的深米学习损失相比, 此函数利用回归问题中的标签排序顺序。 为此, 我们首先显示, 当将不同标签的数据点排在嵌入空间内时, 并按统一的角度排列数据点时损失最小化。 然后, 为了测量其性能, 我们应用了拟议的损失, 在具有挑战性的情况下, 用短距离雷达计数的人的回归任务, 即汽车舱。 引入的方法将准确性以及相邻标签的准确性分别提高到83.0%和99.9%: 在最先进的方法上, 分别增加了6. 7% 和 2.1% 。