  Visual attention has proven to be effective in improving the performance of
person re-identification. Most existing methods apply visual attention
heuristically by learning an additional attention map to re-weight the feature
maps for person re-identification. However, this kind of methods inevitably
increase the model complexity and inference time. In this paper, we propose to
incorporate the attention learning as additional objectives in a person ReID
network without changing the original structure, thus maintain the same
inference time and model size. Two kinds of attentions have been considered to
make the learned feature maps being aware of the person and related body parts
respectively. Globally, a holistic attention branch (HAB) makes the feature
maps obtained by backbone focus on persons so as to alleviate the influence of
background. Locally, a partial attention branch (PAB) makes the extracted
features be decoupled into several groups and be separately responsible for
different body parts (i.e., keypoints), thus increasing the robustness to pose
variation and partial occlusion. These two kinds of attentions are universal
and can be incorporated into existing ReID networks. We have tested its
performance on two typical networks (TriNet and Bag of Tricks) and observed
significant performance improvement on five widely used datasets.
