论文标题
Strdan：用于重新识别的车辆的合成域自适应网络
StRDAN: Synthetic-to-Real Domain Adaptation Network for Vehicle Re-Identification
论文作者
论文摘要
车辆重新识别旨在从车辆图像中获取相同的车辆。这是具有挑战性的，但对于分析和预测城市的交通流量至关重要。尽管深度学习方法已在这项任务中取得了巨大的进步，但它们的大数据要求是一个至关重要的缺点。因此，我们提出了一个合成到现实的域自适应网络（Strdan）框架，该框架可以通过廉价的大规模合成和真实数据进行训练，以提高性能。 Strdan训练方法结合了域的适应性和半监督的学习方法及其相关的损失。 STRDAN比基线模型提供了显着改进，该模型只能使用真实数据对Veri和CityFlow-Reid数据集进行培训，分别实现了3.1％和12.9％的提高平均平均精度。
Vehicle re-identification aims to obtain the same vehicles from vehicle images. This is challenging but essential for analyzing and predicting traffic flow in the city. Although deep learning methods have achieved enormous progress for this task, their large data requirement is a critical shortcoming. Therefore, we propose a synthetic-to-real domain adaptation network (StRDAN) framework, which can be trained with inexpensive large-scale synthetic and real data to improve performance. The StRDAN training method combines domain adaptation and semi-supervised learning methods and their associated losses. StRDAN offers significant improvement over the baseline model, which can only be trained using real data, for VeRi and CityFlow-ReID datasets, achieving 3.1% and 12.9% improved mean average precision, respectively.
