  Person re-identification (ReID) is a challenging task due to arbitrary human
pose variations, background clutters, etc. It has been studied extensively in
recent years, but the multifarious local and global features are still not
fully exploited by either ignoring the interplay between whole-body images and
body-part images or missing in-depth examination of specific body-part images.
In this paper, we propose a novel attention-driven multi-branch network that
learns robust and discriminative human representation from global whole-body
images and local body-part images simultaneously. Within each branch, an
intra-attention network is designed to search for informative and
discriminative regions within the whole-body or body-part images, where
attention is elegantly decomposed into spatial-wise attention and channel-wise
attention for effective and efficient learning. In addition, a novel
inter-attention module is designed which fuses the output of intra-attention
networks adaptively for optimal person ReID. The proposed technique has been
evaluated over three widely used datasets CUHK03, Market-1501 and
DukeMTMC-ReID, and experiments demonstrate its superior robustness and
effectiveness as compared with the state of the arts.
