Cross Domain Knowledge Transfer for Unsupervised Vehicle Re-identification
This addresses domain adaptation for vehicle re-identification, which is an incremental improvement in a specific computer vision task.
The paper tackles the problem of domain bias in vehicle re-identification by proposing a domain adaptation framework with an image-to-image translation network and an attention-based feature learning network, achieving excellent performance on the VehicleID dataset.
Vehicle re-identification (reID) is to identify a target vehicle in different cameras with non-overlapping views. When deploy the well-trained model to a new dataset directly, there is a severe performance drop because of differences among datasets named domain bias. To address this problem, this paper proposes an domain adaptation framework which contains an image-to-image translation network named vehicle transfer generative adversarial network (VTGAN) and an attention-based feature learning network (ATTNet). VTGAN could make images from the source domain (well-labeled) have the style of target domain (unlabeled) and preserve identity information of source domain. To further improve the domain adaptation ability for various backgrounds, ATTNet is proposed to train generated images with the attention structure for vehicle reID. Comprehensive experimental results clearly demonstrate that our method achieves excellent performance on VehicleID dataset.