  This paper considers vehicle re-identification (re-ID) problem. The extreme
viewpoint variation (up to 180 degrees) poses great challenges for existing
approaches. Inspired by the behavior in human's recognition process, we propose
a novel viewpoint-aware metric learning approach. It learns two metrics for
similar viewpoints and different viewpoints in two feature spaces,
respectively, giving rise to viewpoint-aware network (VANet). During training,
two types of constraints are applied jointly. During inference, viewpoint is
firstly estimated and the corresponding metric is used. Experimental results
confirm that VANet significantly improves re-ID accuracy, especially when the
pair is observed from different viewpoints. Our method establishes the new
state-of-the-art on two benchmarks.
