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 will be
available upon acceptance.

本文提出了一个简单的序列预测框架 Ord2Seq，将有序回归任务转化为序列预测过程，以便细致地区分相邻类别，实验证明细致区分相邻类别能够显著提高性能，并且新方法在四个不同场景中超越了现有最先进方法的表现。