  In recent years, the research community has approached the problem of vehicle
re-identification (re-id) with attention-based models, specifically focusing on
regions of a vehicle containing discriminative information. These re-id methods
rely on expensive key-point labels, part annotations, and additional attributes
including vehicle make, model, and color. Given the large number of vehicle
re-id datasets with various levels of annotations, strongly-supervised methods
are unable to scale across different domains. In this paper, we present
Self-supervised Attention for Vehicle Re-identification (SAVER), a novel
approach to effectively learn vehicle-specific discriminative features. Through
extensive experimentation, we show that SAVER improves upon the
state-of-the-art on challenging VeRi, VehicleID, Vehicle-1M and VERI-Wild
datasets.
