Learning from a label distribution has achieved promising results on ordinal
regression tasks such as facial age and head pose estimation wherein, the
concept of adaptive label distribution learning (ALDL) has drawn lots of
attention recently for its superiority in theory. However, compared with the
methods assuming fixed form label distribution, ALDL methods have not achieved
better performance. We argue that existing ALDL algorithms do not fully exploit
the intrinsic properties of ordinal regression. In this paper, we emphatically
summarize that learning an adaptive label distribution on ordinal regression
tasks should follow three principles. First, the probability corresponding to
the ground-truth should be the highest in label distribution. Second, the
probabilities of neighboring labels should decrease with the increase of
distance away from the ground-truth, i.e., the distribution is unimodal. Third,
the label distribution should vary with samples changing, and even be distinct
for different instances with the same label, due to the different levels of
difficulty and ambiguity. Under the premise of these principles, we propose a
novel loss function for fully adaptive label distribution learning, namely
unimodal-concentrated loss. Specifically, the unimodal loss derived from the
learning to rank strategy constrains the distribution to be unimodal.
Furthermore, the estimation error and the variance of the predicted
distribution for a specific sample are integrated into the proposed
concentrated loss to make the predicted distribution maximize at the
ground-truth and vary according to the predicting uncertainty. Extensive
experimental results on typical ordinal regression tasks including age and head
pose estimation, show the superiority of our proposed unimodal-concentrated
loss compared with existing loss functions.

通过三个原则，我们提出了一种全面自适应的标签分布学习方法，称为单峰浓缩损失方法，它使用学习排序策略获得单峰分布，并将估计误差和预测分布的方差集成到损失中，具有在年龄和头部姿态估计等回归任务上表现优越的结果。