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

• URL: http://arxiv.org/abs/2601.03056v1
• Date: Tue, 06 Jan 2026 14:39:09 GMT
• Title: Fine-Grained Generalization via Structuralizing Concept and Feature Space into Commonality, Specificity and Confounding
• Authors: Zhen Wang, Jiaojiao Zhao, Qilong Wang, Yongfeng Dong, Wenlong Yu,
• Abstract summary: Fine-Grained Domain Generalization (FGDG) presents greater challenges than conventional domain generalization.<n>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.<n>We propose Concept-Feature Structuralized Generalization (CFSG) to disentangle concept and feature spaces into three structured components.<n>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%.
• Score: 18.00755828701667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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