  Visible-infrared cross-modality person re-identification (VI-ReID), whose aim
is to match person images between visible and infrared modality, is a
challenging cross-modality image retrieval task. Most existing works integrate
batch normalization layers into their neural network, but we found out that
batch normalization layers would lead to two types of distribution gap: 1)
inter-mini-batch distribution gap -- the distribution gap of the same modality
between each mini-batch; 2) intra-mini-batch modality distribution gap -- the
distribution gap of different modality within the same mini-batch. To address
these problems, we propose a new batch normalization layer called Modality
Batch Normalization (MBN), which normalizes each modality sub-mini-batch
respectively instead of the whole mini-batch, and can reduce these distribution
gap significantly. Extensive experiments show that our MBN is able to boost the
performance of VI-ReID models, even with different datasets, backbones and
losses.
