Ordinal regression refers to classifying object instances into ordinal categories. It has been widely studied in many scenarios, such as medical disease grading, movie rating, etc. Known methods focused only on learning inter-class ordinal relationships, but still incur limitations in distinguishing adjacent categories thus far. In this paper, we propose a simple sequence prediction framework for ordinal regression called Ord2Seq, which, for the first time, transforms each ordinal category label into a special label sequence and thus regards an ordinal regression task as a sequence prediction process. In this way, we decompose an ordinal regression task into a series of recursive binary classification steps, so as to subtly distinguish adjacent categories. Comprehensive experiments show the effectiveness of distinguishing adjacent categories for performance improvement and our new approach exceeds state-of-the-art performances in four different scenarios. Codes are available at https://github.com/wjh892521292/Ord2Seq.

翻译：有序回归是指将目标实例分类到有序类别中。该问题已在诸多场景中得到广泛研究，例如医学疾病分级、电影评分等。现有方法仅关注于学习类别间的有序关系，但在区分相邻类别方面仍存在局限性。本文提出一种用于有序回归的简单序列预测框架，名为Ord2Seq，该框架首次将每个有序类别标签转化为特殊标签序列，从而将有序回归任务视为序列预测过程。通过这种方式，我们将有序回归任务分解为一系列递归的二分类步骤，从而巧妙地区分相邻类别。综合实验表明，区分相邻类别对性能提升具有有效性，且我们的新方法在四个不同场景中均超越了当前最优性能。代码已开源至https://github.com/wjh892521292/Ord2Seq。