Contrastive Order Learning: A General Framework for Ordinal Regression

• URL: http://arxiv.org/abs/2607.08109v1
• Date: Thu, 09 Jul 2026 04:48:26 GMT
• Title: Contrastive Order Learning: A General Framework for Ordinal Regression
• Abstract summary: ConOrd is a contrastive learning framework for ordinal regression.<n>It integrates the strengths of contrastive learning and order learning.<n>It consistently achieves state-of-the-art performance and generalizes well across diverse ordinal regression scenarios.
• Score: 17.77573567161977
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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