  Recently, Synthetic data-based Instance Segmentation has become an
exceedingly favorable optimization paradigm since it leverages simulation
rendering and physics to generate high-quality image-annotation pairs. In this
paper, we propose a Parallel Pre-trained Transformers (PPT) framework to
accomplish the synthetic data-based Instance Segmentation task. Specifically,
we leverage the off-the-shelf pre-trained vision Transformers to alleviate the
gap between natural and synthetic data, which helps to provide good
generalization in the downstream synthetic data scene with few samples.
Swin-B-based CBNet V2, SwinL-based CBNet V2 and Swin-L-based Uniformer are
employed for parallel feature learning, and the results of these three models
are fused by pixel-level Non-maximum Suppression (NMS) algorithm to obtain more
robust results. The experimental results reveal that PPT ranks first in the
CVPR2022 AVA Accessibility Vision and Autonomy Challenge, with a 65.155% mAP.
