  This paper presents our proposed methods for domain adaptive pedestrian
re-identification (Re-ID) task in Visual Domain Adaptation Challenge
(VisDA-2020). 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.
At the first stage, a baseline model is trained with images transferred from
source domain to target domain and from single camera to multiple camera
styles. Then we introduced a domain adaptation framework to train the model on
source data and target data simultaneously. Different pseudo label generation
strategies are adopted to continuously improve the discriminative ability of
the model. Finally, with multiple models ensembled and additional post
processing approaches adopted, our methods achieve 76.56% mAP and 84.25% rank-1
on the test set. Codes are available at
https://github.com/vimar-gu/Bias-Eliminate-DA-ReID
