MFEN:Multi-Frequency Expert Network for Visible-Infrared Person Re-ID
This work addresses the modality discrepancy problem in visible-infrared person re-identification, a domain-specific task, by improving frequency-based learning.
The paper proposes a Multi-Frequency Expert Network (MFEN) for visible-infrared person re-identification, which adaptively combines multiple frequency bands via a mixture-of-experts design, along with Random Frequency Augmentation and Frequency Auxiliary Optimization. The method achieves state-of-the-art results on three VI-ReID datasets, e.g., 82.4% Rank-1 on SYSU-MM01.
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.