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.

The increasing popularity of online food blogs and food ordering services has made personalized recipe recommendation a vital aspect of our emotional well-being. However, existing solutions, mainly based on graph neural networks, still face significant challenges, such as (a) focusing on exploiting the user-recipe interactions while neglecting other crucial pairwise and high-order relationships, and (b) failing to explicitly distinguish the distinct factors, e.g., hedonic and healthy, that influence recipe selection. To address these issues, we propose a progressively-passing-then-disentangling approach named P2D. Our approach utilizes a three-stage progressive message-passing mechanism for better representation learning. Specifically, we incorporate the extra pairwise relationships between recipes and nutrients, ingredients, and visual contents to create fine-grained and multimodal recipe representations. We next refine these representations via message passing between high-order recipe relationships to learn people's shared food preferences. Based on them, we could derive comprehensive user representations, which are subsequently transformed into disentangled forms that correspond to various decision factors through contrastive and mutual information regularization. Experimental results demonstrate both the superiority and the rationality of our method: (a) P2D outperforms the state-of-the-art recipe recommendation methods by a large margin under various metrics, (b) ablation studies confirm the positive impact of each of its components, and (c) our visualization analysis empirically supports the advantage of explicitly disentangling decision factors.