Visible-infrared person re-identification (VI-ReID) is challenging due to the large modality discrepancy between visible and infrared images. We contend that this discrepancy is largely related to differing lighting conditions, including differences in light wavelength and light source type. Recently, frequency-based VI-ReID approaches have achieved notable success because frequency information can better extract identity-relevant contours and details while excluding irrelevant lighting and color. However, existing methods either do not distinguish different frequency bands or focus on only one band, which is insufficient under diverse lighting conditions. To perform comprehensive frequency domain learning, we propose a Multi-Frequency Expert Network (MFEN) that enables multi-frequency modulation and adaptively combines different bands through a mixture-of-experts design. We further introduce Random Frequency Augmentation (RFA) and Frequency Auxiliary Optimization (FAO) to better train MFEN. The three modules are complementary and jointly capture critical frequency-domain details for robust representation learning. Extensive experiments on three VI-ReID datasets demonstrate the effectiveness of our approach.

翻译：可见光-红外行人重识别（VI-ReID）因可见光与红外图像之间巨大的模态差异而极具挑战性。我们认为这种差异主要源于光照条件的不同，包括光波长和光源类型的差异。近年来，基于频率的VI-ReID方法取得了显著成功，因为频率信息能更好地提取与身份相关的轮廓与细节，同时排除无关的光照与色彩。然而，现有方法要么未区分不同频段，要么仅关注单一频段，在多样化光照条件下表现不足。为实现全面的频域学习，我们提出了一种多频率专家网络（MFEN），它通过专家混合设计实现多频率调制并自适应组合不同频段。我们进一步引入了随机频率增强（RFA）和频率辅助优化（FAO）以更好地训练MFEN。这三个模块相互补充，共同捕获关键的频域细节以实现鲁棒的表征学习。在三个VI-ReID数据集上的大量实验验证了我们方法的有效性。