Cross domain knowledge learning with dual-branch adversarial network for vehicle re-identification

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 promotes the images from the source domain (well-labeled) to learn the style of the target domain (unlabeled). Specially, DAN doesn’t need any annotation and can preserve identity information from source domain before and after translation. Furthermore, the generated images are employed to train the vehicle reID model by a proposed attention-based feature learning network. Through the proposed framework, the well-trained reID model has better generalization ability for various scenes in real-world situations. Comprehensive experimental results have demonstrated that our proposed DAVR can achieve excellent performances on benchmark datasets VehicleID and VeRi-776.