  In this paper, we propose an attention pyramid method for person
re-identification. Unlike conventional attention-based methods which only learn
a global attention map, our attention pyramid exploits the attention regions in
a multi-scale manner because human attention varies with different scales. Our
attention pyramid imitates the process of human visual perception which tends
to notice the foreground person over the cluttered background, and further
focus on the specific color of the shirt with close observation. Specifically,
we describe our attention pyramid by a "split-attend-merge-stack" principle. We
first split the features into multiple local parts and learn the corresponding
attentions. Then, we merge local attentions and stack these merged attentions
with the residual connection as an attention pyramid. The proposed attention
pyramid is a lightweight plug-and-play module that can be applied to
off-the-shelf models. We implement our attention pyramid method in two
different attention mechanisms including channel-wise attention and spatial
attention. We evaluate our method on four largescale person re-identification
benchmarks including Market-1501, DukeMTMC, CUHK03, and MSMT17. Experimental
results demonstrate the superiority of our method, which outperforms the
state-of-the-art methods by a large margin with limited computational cost.
