Deep Domain Adaptation on Vehicle Re-Identification
Yifeng Wang, Dan Zeng · 2019
For vehicle re-identification, it is a task of searching all pictures in gallery and finding all vehicle images with the same ID as the given images. Despite using deep learning, we have achieved excellent results in vehicle re-identification. However, there is a huge challenge in vehicle re-identification. The vehicle model we have trained can only work well on particular set of datasets. When transferring to other datasets, its performance is not satisfactory. Domain adaptation is mainly used to solve the problem. As far as we know, the paper first use domain adaptation for vehicle re-identification, used to improve vehicle re-identification in cross dataset performance. The paper uses resnet as the basic skeleton network, adding Maximum Mean Discrepancy (MMD) to the optimization goal of the network, and extend it to multiple-kernel. It is found that the performance of vehicle re-identification has been improved on the basis of transferring directly through experiments.