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.

翻译：细粒度域泛化（FGDG）比传统域泛化面临更大挑战，这源于细粒度识别任务中固有的细微类间差异与相对显著的类内变化。在域偏移下，模型对细粒度线索变得过度敏感，导致关键特征被抑制，性能显著下降。认知研究表明，人类通过利用共性与特性属性对物体进行分类，从而能够准确区分细粒度类别。然而，当前的深度学习模型尚未有效融入这一机制。受此机制启发，我们提出了概念-特征结构化泛化（CFSG）。该模型显式地将概念空间与特征空间解耦为三个结构化成分：共性、特性与混杂片段。为缓解不同程度分布偏移的不利影响，我们引入了一种自适应机制，动态调整共性、特性与混杂成分的比例。在最终预测中，为每对成分分配显式权重。在三个单源基准数据集上的大量实验表明，CFSG 相比基线模型平均性能提升 9.87%，并优于现有最先进方法平均 3.08%。此外，可解释性分析验证了 CFSG 有效整合了多粒度结构化知识，并证实特征结构化促进了概念结构化的涌现。