Contrastive Order Learning: A General Framework for Ordinal Regression

2026-07-09

AI Summary

This work addresses the limitations of existing contrastive learning methods, which overlook ordinal relationships among labels, and conventional ordinal learning approaches, which struggle to capture global ordinal structures. To bridge this gap, the authors propose ConOrd, a novel contrastive ordinal learning framework that seamlessly integrates contrastive learning with ordinal regression. ConOrd introduces rank-difference-based soft affinities and disparity weights to enable fine-grained modeling of ordinal relationships across all sample pairs within a batch, thereby effectively capturing global ordinal structure. The method achieves state-of-the-art performance on diverse tasks including facial age estimation and blind image and video quality assessment, demonstrating strong generalization capability.

Abstract

We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning. While contrastive learning effectively leverages all samples in a batch, it typically ignores the inherent ordering among rank labels. Conversely, order learning explicitly models label ordinality but often relies on local, margin-based comparisons, limiting its ability to capture global ordinal structure. ConOrd addresses these limitations by introducing a contrastive order loss with soft affinity and disparity weights based on rank differences, enabling fine-grained modeling of ordinal relationships across all sample pairs within a batch. Extensive experiments on a range of ordinal regression tasks, including facial age estimation, blind image quality assessment, and blind video quality assessment, demonstrate that ConOrd consistently achieves state-of-the-art performance and generalizes well across diverse ordinal regression scenarios. The source code is available at https://github.com/cwlee00/ConOrd.

Problem

• ordinal regression
• contrastive learning
• order learning
• rank labels
• global ordinal structure

Innovation

• contrastive learning
• ordinal regression
• order learning
• contrastive order loss
• rank-aware modeling