ProtoHPE: Prototype-guided High-frequency Patch Enhancement for
  Visible-Infrared Person Re-identification
Guiwei Zhang Yongfei Zhang Zichang Tan
Visible-infrared person re-identification is challenging due to the large
modality gap. To bridge the gap, most studies heavily rely on the correlation
of visible-infrared holistic person images, which may perform poorly under
severe distribution shifts. In contrast, we find that some cross-modal
correlated high-frequency components contain discriminative visual patterns and
are less affected by variations such as wavelength, pose, and background
clutter than holistic images. Therefore, we are motivated to bridge the
modality gap based on such high-frequency components, and propose
\textbf{Proto}type-guided \textbf{H}igh-frequency \textbf{P}atch
\textbf{E}nhancement (ProtoHPE) with two core designs. \textbf{First}, to
enhance the representation ability of cross-modal correlated high-frequency
components, we split patches with such components by Wavelet Transform and
exponential moving average Vision Transformer (ViT), then empower ViT to take
the split patches as auxiliary input. \textbf{Second}, to obtain semantically
compact and discriminative high-frequency representations of the same identity,
we propose Multimodal Prototypical Contrast. To be specific, it hierarchically
captures the comprehensive semantics of different modal instances, facilitating
the aggregation of high-frequency representations belonging to the same
identity. With it, ViT can capture key high-frequency components during
inference without relying on ProtoHPE, thus bringing no extra complexity.
Extensive experiments validate the effectiveness of ProtoHPE.