Joint Image and Feature Levels Disentanglement for Generalizable Vehicle Re-identification

Domain generalization (DG), which doesn't require any data from target domains during training, is more challenging but practical than unsupervised domain adaptation (UDA). Since different vehicles of the same type have a similar appearance, neural networks always rely on a small amount of useful information to distinguish them, meaning that is more significant to remove ID-unrelated information for vehicle re-Identification (re-ID). Therefore, it is the key to eliminating the interference of a large amount of redundant information for the generalizable vehicle re-ID method. To address this unique challenge, we propose a novel disentanglement learning method that encourages variational autoencoder (VAE) network to reduce ID-unrelated features of vehicles by minimizing image reconstruction errors and providing sufficient representation to vehicle labels. To capture the intrinsic characteristics associated with the DG task, our core idea is to build the identity information streaming framework to separate ID-related and ID-unrelated information at the image and feature levels. In contrast with the general decoupling methods, our method leverages the decoupling of joint image and feature levels to extract more generalizable features. Furthermore, we present a brand-new vehicle dataset of truck types named "Optimus Prime (Opri)", which includes multiple images of each truck captured by cameras at different high-speed toll gates. Experimental results on public datasets demonstrate that our method can achieve promising results and outperform several state-of-the-art approaches. Our codes and models are available at JIFD.