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Parallel Pre-trained Transformers (PPT) for Synthetic Data-based Instance Segmentation
- Publication Year :
- 2022
-
Abstract
- 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.<br />Comment: The solution of 1st Place in AVA Accessibility Vision and Autonomy Challenge on CVPR 2022 workshop. Website: https://accessibility-cv.github.io/
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2206.10845
- Document Type :
- Working Paper