  Vehicle re-identification is one of the core technologies of intelligent
transportation systems and smart cities, but large intra-class diversity and
inter-class similarity poses great challenges for existing method. In this
paper, we propose a multi-guided learning approach which utilizing the
information of attributes and meanwhile introducing two novel random augments
to improve the robustness during training. What's more, we propose an attribute
constraint method and group re-ranking strategy to refine matching results. Our
method achieves mAP of 66.83% and rank-1 accuracy 76.05% in the CVPR 2020 AI
City Challenge.
