Person re-identification (re-id) has made great progress in recent years, but
occlusion is still a challenging problem which significantly degenerates the
identification performance. In this paper, we design a teacher-student learning
framework to learn an occlusion-robust model from the full-body person domain
to the occluded person domain. Notably, the teacher network only uses
large-scale full-body person data to simulate the learning process of occluded
person re-id. Based on the teacher network, the student network then trains a
better model by using inadequate real-world occluded person data. In order to
transfer more knowledge from the teacher network to the student network, we
equip the proposed framework with a co-saliency network and a cross-domain
simulator. The co-saliency network extracts the backbone features, and two
separated collaborative branches are followed by the backbone. One branch is a
classification branch for identity recognition and the other is a co-saliency
branch for guiding the network to highlight meaningful parts without any manual
annotation. The cross-domain simulator generates artificial occlusions on
full-body person data under a growing probability so that the teacher network
could train a cross-domain model by observing more and more occluded cases.
Experiments on four occluded person re-id benchmarks show that our method
outperforms other state-of-the-art methods.

本文提出一种教师 - 学生学习框架，通过构建共同显著特征提取网络和跨域模拟器，从全身人的领域中学习抗遮挡模型，并将该知识转移至真实遮挡人体数据以实现更好的人重识别性能。实验结果在四个参考数据集上表明本文方法优于其他最先进的方法。