  The recent research for person re-identification has been focused on two
trends. One is learning the part-based local features to form more informative
feature descriptors. The other is designing effective metric learning loss
functions such as the triplet loss family. We argue that learning global
features with classification loss could achieve the same goal, even with some
simple and cost-effective architecture design. In this paper, we first explain
why the person re-id framework with standard classification loss usually has
inferior performance compared to metric learning. Based on that, we further
propose a person re-id framework featured by channel grouping and multi-branch
strategy, which divides global features into multiple channel groups and learns
the discriminative channel group features by multi-branch classification
layers. The extensive experiments show that our framework outperforms prior
state-of-the-arts in terms of both accuracy and inference speed.
