  Attention has become more attractive in person reidentification (ReID) as it
is capable of biasing the allocation of available resources towards the most
informative parts of an input signal. However, state-of-the-art works
concentrate only on coarse or first-order attention design, e.g. spatial and
channels attention, while rarely exploring higher-order attention mechanism. We
take a step towards addressing this problem. In this paper, we first propose
the High-Order Attention (HOA) module to model and utilize the complex and
high-order statistics information in attention mechanism, so as to capture the
subtle differences among pedestrians and to produce the discriminative
attention proposals. Then, rethinking person ReID as a zero-shot learning
problem, we propose the Mixed High-Order Attention Network (MHN) to further
enhance the discrimination and richness of attention knowledge in an explicit
manner. Extensive experiments have been conducted to validate the superiority
of our MHN for person ReID over a wide variety of state-of-the-art methods on
three large-scale datasets, including Market-1501, DukeMTMC-ReID and CUHK03-NP.
Code is available at http://www.bhchen.cn/.
