{"ID":6267034,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-13T01:02:08.706470581Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.08109","arxiv_id":"2607.08109","title":"Contrastive Order Learning: A General Framework for Ordinal Regression","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.","short_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. Convers...","url_abs":"https://arxiv.org/abs/2607.08109","url_pdf":"https://arxiv.org/pdf/2607.08109v1","authors":"[\"Chaewon Lee\",\"BeomJun Shim\",\"Kwang Pyo Choi\",\"Chang-Su Kim\"]","published":"2026-07-09T04:48:26Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":614074,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-10T01:11:38.759438437Z","DeletedAt":null,"paper_id":6267034,"paper_url":"https://arxiv.org/abs/2607.08109","paper_title":"Contrastive Order Learning: A General Framework for Ordinal Regression","repo_url":"https://github.com/cwlee00/ConOrd","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}