We mainly focus on four points, i. e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge.
Extracting robust feature representation is one of the key challenges in object re-identification (ReID).
Ranked #4 on Vehicle Re-Identification on VeRi-776
Considering the large gap between the source domain and target domain, we focused on solving two biases that influenced the performance on domain adaptive pedestrian Re-ID and proposed a two-stage training procedure.
Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID.