Going Beyond Real Data - A Robust Visual Representation for Vehicle Re-identification.

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 restricting 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.