  Visible-Infrared Person Re-Identification (VI-ReID) is a challenging
retrieval task under complex modality changes. Existing methods usually focus
on extracting discriminative visual features while ignoring the reliability and
commonality of visual features between different modalities. In this paper, we
propose a novel deep learning framework named Progressive Modality-shared
Transformer (PMT) for effective VI-ReID. To reduce the negative effect of
modality gaps, we first take the gray-scale images as an auxiliary modality and
propose a progressive learning strategy. Then, we propose a Modality-Shared
Enhancement Loss (MSEL) to guide the model to explore more reliable identity
information from modality-shared features. Finally, to cope with the problem of
large intra-class differences and small inter-class differences, we propose a
Discriminative Center Loss (DCL) combined with the MSEL to further improve the
discrimination of reliable features. Extensive experiments on SYSU-MM01 and
RegDB datasets show that our proposed framework performs better than most
state-of-the-art methods. For model reproduction, we release the source code at
https://github.com/hulu88/PMT.
