  In this paper, we present an efficient spatial-temporal representation for
video person re-identification (reID). Firstly, we propose a Bilateral
Complementary Network (BiCnet) for spatial complementarity modeling.
Specifically, BiCnet contains two branches. Detail Branch processes frames at
original resolution to preserve the detailed visual clues, and Context Branch
with a down-sampling strategy is employed to capture long-range contexts. On
each branch, BiCnet appends multiple parallel and diverse attention modules to
discover divergent body parts for consecutive frames, so as to obtain an
integral characteristic of target identity. Furthermore, a Temporal Kernel
Selection (TKS) block is designed to capture short-term as well as long-term
temporal relations by an adaptive mode. TKS can be inserted into BiCnet at any
depth to construct BiCnetTKS for spatial-temporal modeling. Experimental
results on multiple benchmarks show that BiCnet-TKS outperforms
state-of-the-arts with about 50% less computations. The source code is
available at https://github.com/ blue-blue272/BiCnet-TKS.
