发表机构
Faculty of Engineering and Natural Sciences, Sabancı University（工程与自然科学学院，萨班哲大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究多标签遥感场景分类中的域泛化问题，提出标签解耦增强框架，将风格扰动限制在特定标签区域，通过特定方法产生六个变体进行评估，该框架提升了泛化精度，增加参数少且推理不变。
AI 中文摘要
多标签分类为每个航拍场景分配多个同时出现的标签，但部署的模型经常遇到与其训练时不同的数据分布。诸如MixStyle、EFDMix和相关风格不确定性等特征统计增强方法以低成本提高了泛化能力，但会全局干扰通道统计，将每个图像视为单一风格，一个类别可能会污染另一个类别的增强。多标签遥感中的域泛化研究不足，没有先前的方法或多源基准针对此。因此提出了一个标签解耦增强框架，将风格扰动限制在特定标签区域。从可学习模块或梯度类激活映射中获得的每个标签注意力产生每个标签的特征统计；这些统计与共享当前标签的跨域样本在独立的每个标签系数下混合，并通过注意力加权归一化重新组合特征。三个算子与两个注意力源相结合产生六个变体，在来自多标签UCM、AID和DFC15的六个共享标签的留一域基准上进行评估。平均在三个分割和五个种子上，最佳变体达到71.5%的平均平均精度，比经验风险最小化高出5.0个百分点，比最强的全局统计基线高出1.3个百分点。消融实验表明空间注意力和刷新的定位图最具影响力。该框架最多增加0.35%的参数，推理不变，似乎为基于多标签统计的域泛化提供了一种通用、廉价的升级路径。
英文摘要
Multi-label classification assigns several co-occurring labels to each aerial scene, yet deployed models often encounter data distributions different from their training. Feature-statistics augmentation such as MixStyle, EFDMix, and correlated style uncertainty improves generalization at low cost but perturbs channel statistics globally, treating each image as a single style; one class can then contaminate the augmentation of another. Domain generalization is understudied for multi-label remote sensing; no prior method or multi-source benchmark targets it. A label-decoupled augmentation framework is therefore proposed, confining style perturbation to label-specific regions. Per-label attention, obtained from a learnable module or from gradient class-activation maps, yields per-label feature statistics; these statistics are mixed with cross-domain samples that share present labels, under independent per-label coefficients, and features are recomposed by attention-weighted normalization. Three operators combined with two attention sources produce six variants, evaluated on a leave-one-domain-out benchmark from multi-label UCM, AID, and DFC15 over six shared labels. Averaged over three splits and five seeds, the best variant attains 71.5% mean average precision, exceeding empirical risk minimization by 5.0 points and the strongest global-statistics baseline by 1.3 points, with the largest gain on the hardest transfer (up to 7.7 points). Ablations indicate that spatial attention and refreshed localization maps are most influential. The framework adds at most 0.35% parameters, leaves inference unchanged, and appears to offer a generic, inexpensive upgrade path for multi-label statistics-based domain generalization. Code is available upon acceptance at https://github.com/Alaa-Almouradi/Style-Augmentation-Upgrade.
CommentsThis work has been submitted to the IEEE for possible publication
