Fine-Grained Generalization via Structuralizing Concept and Feature Space into Commonality, Specificity and Confounding

2026-01-06

arXiv.org

AI Summary

In fine-grained domain generalization, models often suffer significant performance degradation under domain shift due to excessive sensitivity to subtle inter-class differences, which suppresses the learning of essential features. Inspired by human cognitive mechanisms that leverage both shared and distinctive attributes for categorization, this work proposes Concept-Feature Structured Generalization (CFSG)—the first approach to explicitly incorporate a shared-specific dichotomy into this task. CFSG structurally disentangles the concept and feature spaces into three components: shared, specific, and confounding. It further introduces an adaptive component modulation scheme and a weighted fusion prediction mechanism. Evaluated on three single-source benchmarks, CFSG outperforms baseline methods by 9.87% and state-of-the-art approaches by 3.08% on average. Interpretability analyses confirm the effective integration of structured knowledge and the emergence of meaningful concepts.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Fine-Grained Domain Generalization (FGDG) presents greater challenges than conventional domain generalization due to the subtle inter-class differences and relatively pronounced intra-class variations inherent in fine-grained recognition tasks. Under domain shifts, the model becomes overly sensitive to fine-grained cues, leading to the suppression of critical features and a significant drop in performance. Cognitive studies suggest that humans classify objects by leveraging both common and specific attributes, enabling accurate differentiation between fine-grained categories. However, current deep learning models have yet to incorporate this mechanism effectively. Inspired by this mechanism, we propose Concept-Feature Structuralized Generalization (CFSG). This model explicitly disentangles both the concept and feature spaces into three structured components: common, specific, and confounding segments. To mitigate the adverse effects of varying degrees of distribution shift, we introduce an adaptive mechanism that dynamically adjusts the proportions of common, specific, and confounding components. In the final prediction, explicit weights are assigned to each pair of components. Extensive experiments on three single-source benchmark datasets demonstrate that CFSG achieves an average performance improvement of 9.87% over baseline models and outperforms existing state-of-the-art methods by an average of 3.08%. Additionally, explainability analysis validates that CFSG effectively integrates multi-granularity structured knowledge and confirms that feature structuralization facilitates the emergence of concept structuralization.

Problem

• Fine-Grained Domain Generalization
• Domain Shift
• Intra-class Variation
• Inter-class Difference
• Feature Suppression

Innovation

• Fine-Grained Domain Generalization
• Concept-Feature Structuralization
• Commonality-Specificity Disentanglement
• Adaptive Component Weighting
• Distribution Shift Robustness