A Fourier-Based Semantic Augmentation for Visible-Thermal Person Re-Identification

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.