Abstract
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 one 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 a simple and cost-effective architecture design. We propose a person re-id framework featured by channel grouping and multi-branch strategy, which divides the global feature into multiple channel groups and learns the discriminative channel group features by multi-branch classification layers. In extensive experiments, our network outperforms state-of-the-art person re-id frameworks in terms of both accuracy and inference cost.
Abstract (translated)
最近关于人员重新识别的研究主要集中在两个趋势上。一个是学习基于部分的局部特征，以形成更多信息的特征描述符。另一个是设计有效的度量学习损失函数，例如Triplet损失族。我们认为，即使采用简单且经济高效的架构设计，学习具有分类丢失的全局特征也可以实现相同的目标。我们提出了一个以信道分组和多分支策略为特征的人重新框架，将全局特征划分为多个信道组，并通过多分支分类层学习判别信道组特征。在广泛的实验中，我们的网络在准确性和推理成本方面优于最先进的人员重建框架。
URL
https://arxiv.org/abs/1809.05864
