  Learning cross-view consistent feature representation is the key for accurate
vehicle Re-identification (ReID), since the visual appearance of vehicles
changes significantly under different viewpoints. To this end, most existing
approaches resort to the supervised cross-view learning using extensive extra
viewpoints annotations, which however, is difficult to deploy in real
applications due to the expensive labelling cost and the continous viewpoint
variation that makes it hard to define discrete viewpoint labels. In this
study, we present a pluggable Weakly-supervised Cross-View Learning (WCVL)
module for vehicle ReID. Through hallucinating the cross-view samples as the
hardest positive counterparts in feature domain, we can learn the consistent
feature representation via minimizing the cross-view feature distance based on
vehicle IDs only without using any viewpoint annotation. More importantly, the
proposed method can be seamlessly plugged into most existing vehicle ReID
baselines for cross-view learning without re-training the baselines. To
demonstrate its efficacy, we plug the proposed method into a bunch of
off-the-shelf baselines and obtain significant performance improvement on four
public benchmark datasets, i.e., VeRi-776, VehicleID, VRIC and VRAI.
