  Vehicle re-identification plays a crucial role in the management of
transportation infrastructure and traffic flow. However, this is a challenging
task due to the large view-point variations in appearance, environmental and
instance-related factors. Modern systems deploy CNNs to produce unique
representations from the images of each vehicle instance. Most work focuses on
leveraging new losses and network architectures to improve the descriptiveness
of these representations. In contrast, our work concentrates on re-ranking and
embedding expansion techniques. We propose an efficient approach for combining
the outputs of multiple models at various scales while exploiting tracklet and
neighbor information, called dual embedding expansion (DEx). Additionally, a
comparative study of several common image retrieval techniques is presented in
the context of vehicle re-ID. Our system yields competitive performance in the
2020 NVIDIA AI City Challenge with promising results. We demonstrate that DEx
when combined with other re-ranking techniques, can produce an even larger gain
without any additional attribute labels or manual supervision.
