标题： 面向目标的单域泛化 显示英文标题

标题： Target-Oriented Single Domain Generalization

摘要： 深度模型在单一源域上训练时，在分布变化下常常会灾难性失败，这是单域泛化（SDG）中的一个关键挑战。 尽管现有方法专注于增强源数据或学习不变特征，但它们忽视了一个易于获取的资源：目标部署环境的文本描述。 我们提出了面向目标的单域泛化（TO-SDG），这是一种新的问题设置，利用目标域的文本描述来引导模型泛化，而无需任何目标数据。 为了解决TO-SDG，我们引入了频谱目标对齐（STAR），这是一个轻量级模块，通过利用视觉语言模型（VLM）如CLIP，将目标语义注入源特征中。 STAR使用从目标描述的文本嵌入中导出的目标锚定子空间，将图像特征重新定位到部署域，然后利用频谱投影保留与目标线索对齐的方向，同时丢弃源特定的噪声。 此外，我们使用视觉语言蒸馏来将主干特征与VLM的语义几何对齐。 STAR进一步采用特征空间Mixup，以确保源和目标导向表示之间的平滑过渡。 在各种图像分类和目标检测基准上的实验表明了STAR的优势。 这项工作证明了最小的文本元数据，这是一种实用且常被忽视的资源，在严重的数据约束下显著增强了泛化能力，为在具有未见数据的目标环境中部署稳健模型开辟了新途径。 显示英文摘要

摘要： Deep models trained on a single source domain often fail catastrophically under distribution shifts, a critical challenge in Single Domain Generalization (SDG). While existing methods focus on augmenting source data or learning invariant features, they neglect a readily available resource: textual descriptions of the target deployment environment. We propose Target-Oriented Single Domain Generalization (TO-SDG), a novel problem setup that leverages the textual description of the target domain, without requiring any target data, to guide model generalization. To address TO-SDG, we introduce Spectral TARget Alignment (STAR), a lightweight module that injects target semantics into source features by exploiting visual-language models (VLMs) such as CLIP. STAR uses a target-anchored subspace derived from the text embedding of the target description to recenter image features toward the deployment domain, then utilizes spectral projection to retain directions aligned with target cues while discarding source-specific noise. Moreover, we use a vision-language distillation to align backbone features with VLM's semantic geometry. STAR further employs feature-space Mixup to ensure smooth transitions between source and target-oriented representations. Experiments across various image classification and object detection benchmarks demonstrate STAR's superiority. This work establishes that minimal textual metadata, which is a practical and often overlooked resource, significantly enhances generalization under severe data constraints, opening new avenues for deploying robust models in target environments with unseen data.