  Person re-identification aims to robustly measure similarities between person
images. The significant variation of person poses and viewing angles challenges
for accurate person re-identification. The spatial layout and correspondences
between query person images are vital information for tackling this problem but
are ignored by most state-of-the-art methods. In this paper, we propose a novel
Kronecker Product Matching module to match feature maps of different persons in
an end-to-end trainable deep neural network. A novel feature soft warping
scheme is designed for aligning the feature maps based on matching results,
which is shown to be crucial for achieving superior accuracy. The multi-scale
features based on hourglass-like networks and self-residual attention are also
exploited to further boost the re-identification performance. The proposed
approach outperforms state-of-the-art methods on the Market-1501, CUHK03, and
DukeMTMC datasets, which demonstrates the effectiveness and generalization
ability of our proposed approach.
