  Person re-identification becomes a more and more important task due to its
wide applications. In practice, person re-identification still remains
challenging due to the variation of person pose, different lighting, occlusion,
misalignment, background clutter, etc. In this paper, we propose a multi-scale
body-part mask guided attention network (MMGA), which jointly learns whole-body
and part body attention to help extract global and local features
simultaneously. In MMGA, body-part masks are used to guide the training of
corresponding attention. Experiments show that our proposed method can reduce
the negative influence of variation of person pose, misalignment and background
clutter. Our method achieves rank-1/mAP of 95.0%/87.2% on the Market1501
dataset, 89.5%/78.1% on the DukeMTMC-reID dataset, outperforming current
state-of-the-art methods.
