  Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across
different cameras, hence plays an important role in modern traffic management
systems. The technical challenges require the algorithms must be robust in
different views, resolution, occlusion and illumination conditions. In this
paper, we first analyze the main factors hindering the Vehicle Re-ID
performance. We then present our solutions, specifically targeting the dataset
Track 2 of the 5th AI City Challenge, including (1) reducing the domain gap
between real and synthetic data, (2) network modification by stacking multi
heads with attention mechanism, (3) adaptive loss weight adjustment. Our method
achieves 61.34% mAP on the private CityFlow testset without using external
dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on
the Veri benchmark. The code is available at
https://github.com/cybercore-co-ltd/track2_aicity_2021.
