Holistic person re-identification (ReID) has received extensive study in the
past few years and achieves impressive progress. However, persons are often
occluded by obstacles or other persons in practical scenarios, which makes
partial person re-identification non-trivial. In this paper, we propose a
spatial-channel parallelism network (SCPNet) in which each channel in the ReID
feature pays attention to a given spatial part of the body. The spatial-channel
corresponding relationship supervises the network to learn discriminative
feature for both holistic and partial person re-identification. The single
model trained on four holistic ReID datasets achieves competitive accuracy on
these four datasets, as well as outperforms the state-of-the-art methods on two
partial ReID datasets without training.

本文提出一种空间通道并行网络（SCPNet），用于全局和局部人员再识别，并在四个全局数据集上训练了一个模型，同时在两个局部数据集上表现出优于最先进方法的准确性。