  Visible-infrared person re-identification (VI-ReID) is challenging due to the
significant cross-modality discrepancies between visible and infrared images.
While existing methods have focused on designing complex network architectures
or using metric learning constraints to learn modality-invariant features, they
often overlook which specific component of the image causes the modality
discrepancy problem. In this paper, we first reveal that the difference in the
amplitude component of visible and infrared images is the primary factor that
causes the modality discrepancy and further propose a novel Frequency Domain
modality-invariant feature learning framework (FDMNet) to reduce modality
discrepancy from the frequency domain perspective. Our framework introduces two
novel modules, namely the Instance-Adaptive Amplitude Filter (IAF) module and
the Phrase-Preserving Normalization (PPNorm) module, to enhance the
modality-invariant amplitude component and suppress the modality-specific
component at both the image- and feature-levels. Extensive experimental results
on two standard benchmarks, SYSU-MM01 and RegDB, demonstrate the superior
performance of our FDMNet against state-of-the-art methods.
