Ordinal regression bridges regression and classification by assigning objects to ordered classes. While human experts rely on discriminative patch-level features for decisions, current approaches are limited by the availability of only image-level ordinal labels, overlooking fine-grained patch-level characteristics. In this paper, we propose a Dual-level Fuzzy Learning with Patch Guidance framework, named DFPG that learns precise feature-based grading boundaries from ambiguous ordinal labels, with patch-level supervision. Specifically, we propose patch-labeling and filtering strategies to enable the model to focus on patch-level features exclusively with only image-level ordinal labels available. We further design a dual-level fuzzy learning module, which leverages fuzzy logic to quantitatively capture and handle label ambiguity from both patch-wise and channel-wise perspectives. Extensive experiments on various image ordinal regression datasets demonstrate the superiority of our proposed method, further confirming its ability in distinguishing samples from difficult-to-classify categories. The code is available atthis https URL.
View on arXiv@article{dong2025_2505.05834,
title={ Dual-level Fuzzy Learning with Patch Guidance for Image Ordinal Regression },
author={ Chunlai Dong and Haochao Ying and Qibo Qiu and Jinhong Wang and Danny Chen and Jian Wu },
journal={arXiv preprint arXiv:2505.05834},
year={ 2025 }
}