Visible-infrared cross-modality person re-identification is an important task in the night video surveillance system, the huge difference between infrared and visible light images makes this work quite challenging. Unlike traditional person re-identification, a cross-modality mission needs to solve intra-class differences and inter-class variations. To solve the problem of huge modality discrepancy, in this paper, we propose a channel-based partition network which can unify the features of the two modes in an end-to-end way. Firstly, to handle the lack of discriminative information, we introduce newly generated samples to help the network improve its ability to learn cross modal features. Secondly, at the feature level, we propose a distinctive method of learning local features, in which the set of feature maps is parted on the channel. At the end of the proposed framework, we add a lightweight feature converter to further eliminate modality differences. The experimental results on the two popular datasets prove the effectiveness of our work.

可见红外跨模态行人再识别是夜间视频监控系统中的一项重要任务，红外和可见光图像之间的巨大差异使得这项工作极具挑战性。与传统的人员重识别不同，跨模态任务需要解决类内差异和类间差异。为了解决模态差异巨大的问题，在本文中，我们提出了一种基于通道的分区网络，可以以端到端的方式统一两种模式的特征。首先，为了解决缺乏判别信息的问题，我们引入了新生成的样本来帮助网络提高学习跨模态特征的能力。其次，在特征层面，我们提出了一种独特的学习局部特征的方法，其中特征图集在通道上分开。在提出的框架的最后，我们添加了一个轻量级特征转换器，以进一步消除模态差异。在两个流行数据集上的实验结果证明了我们工作的有效性。