Dual-level Fuzzy Learning with Patch Guidance for Image Ordinal Regression
Journal:
arXiv
Published Date:
May 9, 2025
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.
