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：无监督领域自适应（UDA）行人重识别（re-ID）旨在从源域中带标签的图像中学习身份信息，并将其应用于目标域中未标记的图像。许多无监督重识别方法的主要问题在于，它们在面对光照、视角和遮挡等较大的领域变化时表现不佳。本文提出了一种合成模型库（SMB）来处理无监督行人重识别中的光照变化问题。该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矩阵组成。它们通过使用不同光照条件下的合成数据进行训练，从而协同作用使得SMB对光照变化具有鲁棒性。为了更准确地量化光照强度并提高合成图像的质量，我们引入了一个新的3D虚拟人类数据集以进行基于生成对抗网络（GAN）的图像合成。实验结果表明，在多个重识别基准上，所提出的SMB优于其他合成方法。