  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.
