Going Beyond Real Data: A Robust Visual Representation for Vehicle Re-Identification

Zhedong Zheng, Minyue Jiang, Zhigang Wang, Jian Wang, Zechen Bai, Xuanmeng Zhang, Xin Yu, Xiao Tan, Yi Yang, Shilei Wen, Errui Ding; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 598-599

In this report, we present the Baidu-UTS submission to the AICity Challenge in CVPR 2020. This is the winning solution to the vehicle re-identification (re-id) track. We focus on developing a robust vehicle re-id system for real-world scenarios. In particular, we aim to fully leverage the merits of the synthetic data while arming with real images to learn a robust representation for vehicles in different views and illumination conditions. By comprehensively investigating and evaluating various data augmentation approaches and popular strong baselines, we analyze the bottleneck restrict- ing the vehicle re-id performance. Based on our analysis, we therefore design a vehicle re-id method with better data augmentation, training and post-processing strategies. Our proposed method has achieved the 1st place out of 41 teams, yielding 84.13% mAP on the private test set. We hope that our practice could shed light on using synthetic and real data effectively in training deep re-id networks and pave the way for real-world vehicle re-id systems.