  In this work, we present our solution to the vehicle re-identification
(vehicle Re-ID) track in AI City Challenge 2020 (AIC2020). The purpose of
vehicle Re-ID is to retrieve the same vehicle appeared across multiple cameras,
and it could make a great contribution to the Intelligent Traffic System(ITS)
and smart city. Due to the vehicle's orientation, lighting and inter-class
similarity, it is difficult to achieve robust and discriminative representation
feature. For the vehicle Re-ID track in AIC2020, our method is to fuse features
extracted from different networks in order to take advantages of these networks
and achieve complementary features. For each single model, several methods such
as multi-loss, filter grafting, semi-supervised are used to increase the
representation ability as better as possible. Top performance in City-Scale
Multi-Camera Vehicle Re-Identification demonstrated the advantage of our
methods, and we got 5-th place in the vehicle Re-ID track of AIC2020. The codes
are available at https://github.com/gggcy/AIC2020_ReID.
