  Vehicle Re-identification is a challenging task due to intra-class
variability and inter-class similarity across non-overlapping cameras. To
tackle these problems, recently proposed methods require additional annotation
to extract more features for false positive image exclusion. In this paper, we
propose a model powered by adaptive attention modules that requires fewer label
annotations but still out-performs the previous models. We also include a
re-ranking method that takes account of the importance of metadata feature
embeddings in our paper. The proposed method is evaluated on CVPR AI City
Challenge 2020 dataset and achieves mAP of 37.25% in Track 2.
