Unsupervised domain adaptive (UDA) person re-identification (re-ID) aims to learn identity information from labeled images in source domains and apply it to unlabeled images in a target domain. One major issue with many unsupervised re-identification methods is that they do not perform well relative to large domain variations such as illumination, viewpoint, and occlusions. In this paper, we propose a Synthesis Model Bank (SMB) to deal with illumination variation in unsupervised person re-ID. The proposed SMB consists of several convolutional neural networks (CNN) for feature extraction and Mahalanobis matrices for distance metrics. They are trained using synthetic data with different illumination conditions such that their synergistic effect makes the SMB robust against illumination variation. To better quantify the illumination intensity and improve the quality of synthetic images, we introduce a new 3D virtual-human dataset for GAN-based image synthesis. From our experiments, the proposed SMB outperforms other synthesis methods on several re-ID benchmarks.

翻译：无监督域自适应（UDA）行人重识别旨在从源域标记图像中学习身份信息，并将其应用于目标域未标记图像。许多无监督重识别方法的一个主要问题在于，它们在光照、视角和遮挡等大域变化下表现不佳。本文提出一种合成模型库（SMB）来处理无监督行人重识别中的光照变化。所提出的SMB由多个用于特征提取的卷积神经网络（CNN）和用于距离度量的马氏距离矩阵组成。这些模型通过不同光照条件下的合成数据进行训练，其协同效应使SMB对光照变化具有鲁棒性。为了更好地量化光照强度并提高合成图像质量，我们引入了一个新的3D虚拟人物数据集用于基于生成对抗网络（GAN）的图像合成。实验结果表明，在多个重识别基准测试中，所提出的SMB优于其他合成方法。