  Recent efforts have shown promising results for person re-identification by
designing part-based architectures to allow a neural network to learn
discriminative representations from semantically coherent parts. Some efforts
use soft attention to reallocate distant outliers to their most similar parts,
while others adjust part granularity to incorporate more distant positions for
learning the relationships. Others seek to generalize part-based methods by
introducing a dropout mechanism on consecutive regions of the feature map to
enhance distant region relationships. However, only few prior efforts model the
distant or non-local positions of the feature map directly for the person re-ID
task. In this paper, we propose a novel attention mechanism to directly model
long-range relationships via second-order feature statistics. When combined
with a generalized DropBlock module, our method performs equally to or better
than state-of-the-art results for mainstream person re-identification datasets,
including Market1501, CUHK03, and DukeMTMC-reID.
