Visible-Infrared Person Re-Identification via Patch-Mixed Cross-Modality Learning

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 ntroduce extra noise, 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. In this way, the modellearns to recognize a person through patches of different styles, and the modality semantic correspondence is directly embodied. 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. In addition, the relationship between identity centers among modalities is explored to further reduce the modality variance, and the global-to-part constraint is introduced to regularize representation learning of part features. On two VI-ReID datasets, we report new state-of-the-art performance with the proposed method.