通过结构化概念和特征空间为共同性、特异性和混淆性实现细粒度泛化
Fine-Grained Generalization via Structuralizing Concept and Feature Space into Commonality, Specificity and Confounding
AI总结：
CFSG通过结构化分解概念和特征空间为共同性、特异性和混淆性，提升细粒度领域泛化性能，实验表明其在多个基准数据集上显著优于现有方法。
摘要：
细粒度领域泛化（FGDG）比传统领域泛化更具挑战性，因为细粒度识别任务中存在细微的类间差异和相对明显的类内变化。在领域转移下，模型对细粒度线索变得过于敏感，导致关键特征被抑制，性能显著下降。认知研究表明，人类通过利用共同和特定属性来分类物体，能够准确区分细粒度类别。然而，当前深度学习模型尚未有效整合这一机制。受此启发，我们提出了概念-特征结构化泛化（CFSG）。该模型明确将概念和特征空间分解为三个结构化部分：共同、特定和混淆段。为缓解分布偏移程度变化的负面影响，我们引入了一种自适应机制，动态调整共同、特定和混淆部分的比例。在最终预测中，为每对部分分配显式权重。在三个单源基准数据集上的广泛实验表明，CFSG在基线模型上平均性能提高了9.87%，并以平均3.08%的优势超越现有最先进方法。此外，可解释性分析验证了CFSG有效整合多粒度结构化知识，并确认特征结构化促进了概念结构化的出现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
