Abstract
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 hourglasslike 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