---
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"
doi: "10.24963/ijcai.2025/103"
url: "https://ijcai.org/proceedings/2025/103"
pdf: "https://ijcai.org/proceedings/2025/0103.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "918-926"
track: "Main Track"
keywords:
  - "Multidisciplinary Topics and Applications: MTA: Health and medicine"
  - "Computer Vision: CV: Recognition (object detection, categorization)"
---

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

Chunlai Dong, Haochao Ying, Qibo Qiu, Jinhong Wang, Danny Chen, Jian Wu

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 918-926. DOI: [10.24963/ijcai.2025/103](https://doi.org/10.24963/ijcai.2025/103)

## 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.