  In this report, we introduce the technical details of our submission to the
VIPriors object detection challenge. Our solution is based on mmdetction of a
strong baseline open-source detection toolbox. Firstly, we introduce an
effective data augmentation method to address the lack of data problem, which
contains bbox-jitter, grid-mask, and mix-up. Secondly, we present a robust
region of interest (ROI) extraction method to learn more significant ROI
features via embedding global context features. Thirdly, we propose a
multi-model integration strategy to refinement the prediction box, which
weighted boxes fusion (WBF). Experimental results demonstrate that our approach
can significantly improve the average precision (AP) of object detection on the
subset of the COCO2017 dataset.
