  The widespread popularization of vehicles has facilitated all people's life
during the last decades. However, the emergence of a large number of vehicles
poses the critical but challenging problem of vehicle re-identification (reID).
Till now, for most vehicle reID algorithms, both the training and testing
processes are conducted on the same annotated datasets under supervision.
However, even a well-trained model will still cause fateful performance drop
due to the severe domain bias between the trained dataset and the real-world
scenes.
  To address this problem, this paper proposes a domain adaptation framework
for vehicle reID (DAVR), which narrows the cross-domain bias by fully
exploiting the labeled data from the source domain to adapt the target domain.
DAVR develops an image-to-image translation network named Dual-branch
Adversarial Network (DAN), which could promote the images from the source
domain (well-labeled) to learn the style of target domain (unlabeled) without
any annotation and preserve identity information from source domain. Then the
generated images are employed to train the vehicle reID model by a proposed
attention-based feature learning model with more reasonable styles. Through the
proposed framework, the well-trained reID model has better domain adaptation
ability for various scenes in real-world situations. Comprehensive experimental
results have demonstrated that our proposed DAVR can achieve excellent
performances on both VehicleID dataset and VeRi-776 dataset.
