Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction
This addresses a known bottleneck in ordinal regression for applications like medical grading and movie ratings, offering an incremental but effective solution.
The paper tackles the problem of distinguishing adjacent categories in ordinal regression by proposing Ord2Seq, a framework that transforms ordinal labels into sequences for recursive binary classification, resulting in state-of-the-art performance improvements across four scenarios.
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.