  In recent years, attention models have been extensively used for person and
vehicle re-identification. Most re-identification methods are designed to focus
attention on key-point locations. However, depending on the orientation, the
contribution of each key-point varies. In this paper, we present a novel
dual-path adaptive attention model for vehicle re-identification (AAVER). The
global appearance path captures macroscopic vehicle features while the
orientation conditioned part appearance path learns to capture localized
discriminative features by focusing attention on the most informative
key-points. Through extensive experimentation, we show that the proposed AAVER
method is able to accurately re-identify vehicles in unconstrained scenarios,
yielding state of the art results on the challenging dataset VeRi-776. As a
byproduct, the proposed system is also able to accurately predict vehicle
key-points and shows an improvement of more than 7% over state of the art. The
code for key-point estimation model is available at
https://github.com/Pirazh/Vehicle_Key_Point_Orientation_Estimation.
