MFEN: Multi-Frequency Expert Network for Visible-Infrared Person Re-ID

Visible-infrared person re-identification (VI-ReID) is a challenging task due to the significant modality discrepancy between visible and infrared images. We contend that the discrepancy primarily arises from varying lighting conditions of the two modality data, including differences in the wavelengths of light and the types of light source. Recently, frequency-based VI-ReID approaches have achieved notable success, since frequency information can more effectively extract contours and details pertinent to identity while excluding irrelevant lighting and color. However, existing methods do not distinguish different frequency bands or focus solely on a particular frequency band, which is insufficient for capturing the inherent variations in frequency 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 frequencies through a mixture-of-experts method. In addition, we further introduce a Random Frequency Augmentation (RFA) and a Frequency Auxiliary Optimization (FAO) to effectively train the MFEN in mining frequency information. The proposed three frequency modules are complementary to each other and adaptively capture critical frequency domain details to achieve robust representations. Extensive experiments on three VI-ReID datasets demonstrate the effectiveness of our approach.