This letter introduces a novel Fourier-based data augmentation strategy for visible-thermal person re-identification (VT-ReID). Different from some existing methods which are proposed from the perspective of network structure and loss functions, our method aims to fully consider the semantic information from the perspective of data preprocessing. The main hypothesis is that the phase component in the Fourier domain contains high-level semantic information and the amplitude component contains low-level modality awareness information. In order to make the model pay more attention to semantic information learning, we design a simple but effective Fourier-based semantic augmentation (FSA) module, which can be inserted seamlessly into any existing models. Extensive experiments on RegDB and SYSU-MM01 datasets have shown that our proposed method can improve the VT-ReID performance significantly and achieve state-of-the-art performance.

中文翻译：

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一种基于傅里叶的语义增强可见热人员重新识别

这封信介绍了一种新颖的基于傅里叶的数据增强策略，用于可见热人员重新识别 (VT-ReID)。与现有的一些从网络结构和损失函数的角度提出的方法不同，我们的方法旨在从数据预处理的角度充分考虑语义信息。主要假设是傅里叶域中的相位分​​量包含高级语义信息，幅度分量包含低级模态感知信息。为了使模型更加关注语义信息学习，我们设计了一个简单但有效的基于傅里叶的语义增强（FSA）模块，可以无缝地插入到任何现有模型中。