  Modern data augmentation using a mixture-based technique can regularize the
models from overfitting to the training data in various computer vision
applications, but a proper data augmentation technique tailored for the
part-based Visible-Infrared person Re-IDentification (VI-ReID) models remains
unexplored. In this paper, we present a novel data augmentation technique,
dubbed PartMix, that synthesizes the augmented samples by mixing the part
descriptors across the modalities to improve the performance of part-based
VI-ReID models. Especially, we synthesize the positive and negative samples
within the same and across different identities and regularize the backbone
model through contrastive learning. In addition, we also present an
entropy-based mining strategy to weaken the adverse impact of unreliable
positive and negative samples. When incorporated into existing part-based
VI-ReID model, PartMix consistently boosts the performance. We conduct
experiments to demonstrate the effectiveness of our PartMix over the existing
VI-ReID methods and provide ablation studies.
