Dual-level Fuzzy Learning with Patch Guidance for Image Ordinal Regression

• URL: http://arxiv.org/abs/2505.05834v2
• Date: Sat, 17 May 2025 09:28:23 GMT
• Title: Dual-level Fuzzy Learning with Patch Guidance for Image Ordinal Regression
• Authors: Chunlai Dong, Haochao Ying, Qibo Qiu, Jinhong Wang, Danny Chen, Jian Wu,
• Abstract summary: Ordinal regression bridges regression and classification by assigning objects to ordered classes.<n>Current approaches are limited by the availability of only image-level ordinal labels.<n>We propose a Dual-level Fuzzy Learning with Patch Guidance framework, named DFPG.
• Score: 8.538034422744005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 at https://github.com/ZJUMAI/DFPG-ord.

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