  Person re-identification aims at finding a person of interest in an image
gallery by comparing the probe image of this person with all the gallery
images. It is generally treated as a retrieval problem, where the affinities
between the probe image and gallery images (P2G affinities) are used to rank
the retrieved gallery images. However, most existing methods only consider P2G
affinities but ignore the affinities between all the gallery images (G2G
affinity). Some frameworks incorporated G2G affinities into the testing
process, which is not end-to-end trainable for deep neural networks. In this
paper, we propose a novel group-shuffling random walk network for fully
utilizing the affinity information between gallery images in both the training
and testing processes. The proposed approach aims at end-to-end refining the
P2G affinities based on G2G affinity information with a simple yet effective
matrix operation, which can be integrated into deep neural networks. Feature
grouping and group shuffle are also proposed to apply rich supervisions for
learning better person features. The proposed approach outperforms
state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets by
large margins, which demonstrate the effectiveness of our approach.
