  Visible-infrared cross-modality person re-identification is a challenging
ReID task, which aims to retrieve and match the same identity's images between
the heterogeneous visible and infrared modalities. Thus, the core of this task
is to bridge the huge gap between these two modalities. The existing
convolutional neural network-based methods mainly face the problem of
insufficient perception of modalities' information, and can not learn good
discriminative modality-invariant embeddings for identities, which limits their
performance. To solve these problems, we propose a cross-modality
transformer-based method (CMTR) for the visible-infrared person
re-identification task, which can explicitly mine the information of each
modality and generate better discriminative features based on it. Specifically,
to capture modalities' characteristics, we design the novel modality
embeddings, which are fused with token embeddings to encode modalities'
information. Furthermore, to enhance representation of modality embeddings and
adjust matching embeddings' distribution, we propose a modality-aware
enhancement loss based on the learned modalities' information, reducing
intra-class distance and enlarging inter-class distance. To our knowledge, this
is the first work of applying transformer network to the cross-modality
re-identification task. We implement extensive experiments on the public
SYSU-MM01 and RegDB datasets, and our proposed CMTR model's performance
significantly surpasses existing outstanding CNN-based methods.
