基于图像合成的光照变化校正用于无监督域自适应行人重识别
Illumination Variation Correction Using Image Synthesis For Unsupervised Domain Adaptive Person Re-Identification
AI总结：
本文提出合成模型库（SMB）结合CNN与马氏距离，利用新3D虚拟数据集生成不同光照合成图像，以校正光照变化，提升无监督域自适应行人重识别性能。
摘要：
无监督域自适应（UDA）行人重识别（re-ID）旨在从源域中的标记图像学习身份信息，并将其应用于目标域中的未标记图像。许多无监督重识别方法的一个主要问题是，它们在面对光照、视角和遮挡等大的域变化时表现不佳。在本文中，我们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。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。它们使用不同光照条件下的合成数据进行训练，使得它们的协同效应使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的质量，我们引入了一个新的3D虚拟人体数据集用于基于GAN的图像合成。从我们的实验来看，所提出的SMB在多个重识别基准上优于其他合成方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
