In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention. Vehicle ReID is challenging due to 1) high intra-class variability (caused by the dependency of shape and appearance on viewpoint), and 2) small inter-class variability (caused by the similarity in shape and appearance between vehicles produced by different manufacturers). To address these challenges, we propose a Pose-Aware Multi-Task Re-Identification (PAMTRI) framework. This approach includes two innovations compared with previous methods. First, it overcomes viewpoint-dependency by explicitly reasoning about vehicle pose and shape via keypoints, heatmaps and segments from pose estimation. Second, it jointly classifies semantic vehicle attributes (colors and types) while performing ReID, through multi-task learning with the embedded pose representations. Since manually labeling images with detailed pose and attribute information is prohibitive, we create a large-scale highly randomized synthetic dataset with automatically annotated vehicle attributes for training. Extensive experiments validate the effectiveness of each proposed component, showing that PAMTRI achieves significant improvement over state-of-the-art on two mainstream vehicle ReID benchmarks: VeRi and CityFlow-ReID. Code and models are available at https://github.com/NVlabs/PAMTRI.

提出了一种基于姿态感知多任务重新识别（PAMTRI）框架的方法来解决车辆重新识别中的高内部类别变异和小外部类别变异的挑战，该方法通过关键点、热图和姿态估计中的片段明确地推理车辆姿态和形状，同时执行ReID，通过嵌入的姿态表示进行多任务学习，同时分类语义车辆属性（颜色和类型），并创建了一个大规模高度随机化的合成数据集，其中包含自动注释的车辆属性，以便进行训练。 

PAMTRI: 基于姿势感知的多任务学习，在高度随机化合成数据下进行车辆再识别