  Visible-infrared person re-identification (VI-ReID) aims to retrieve images
of the same pedestrian from different modalities, where the challenges lie in
the significant modality discrepancy. To alleviate the modality gap, recent
methods generate intermediate images by GANs, grayscaling, or mixup strategies.
However, these methods could introduce extra data distribution, and the
semantic correspondence between the two modalities is not well learned. In this
paper, we propose a Patch-Mixed Cross-Modality framework (PMCM), where two
images of the same person from two modalities are split into patches and
stitched into a new one for model learning. A part-alignment loss is introduced
to regularize representation learning, and a patch-mixed modality learning loss
is proposed to align between the modalities. In this way, the model learns to
recognize a person through patches of different styles, thereby the modality
semantic correspondence can be inferred. In addition, with the flexible image
generation strategy, the patch-mixed images freely adjust the ratio of
different modality patches, which could further alleviate the modality
imbalance problem. On two VI-ReID datasets, we report new state-of-the-art
performance with the proposed method.
