Compared with MS-COCO, the dataset for the competition has a larger proportion of large objects which area is greater than 96x96 pixels. As getting fine boundaries is vitally important for large object segmentation, Mask R-CNN with PointRend is selected as the base segmentation framework to output high-quality object boundaries. Besides, a better engine that integrates ResNeSt, FPN and DCNv2, and a range of effective tricks that including multi-scale training and test time augmentation are applied to improve segmentation performance. Our best performance is an ensemble of four models (three PointRend-based models and SOLOv2), which won the 2nd place in IJCAI-PRICAI 3D AI Challenge 2020: Instance Segmentation.

翻译：与MS-COCO相比,竞争的数据集拥有较大比例的大型物体,其面积大于96x96 像素。由于获得细微的边界对于大型物体分离至关重要,因此将带有点Rn的Mask R-CNN和PointRend选为输出高质量物体界限的基础分解框架。此外,将ResNest、FPN和DCNv2相结合的更好的引擎,以及包括多尺度培训和测试时间增加在内的一系列有效技巧用于改善分解性能。我们的最佳性能是四种模型的组合(三个基于点Rend的模型和SOLOv2),这四个模型在IJCAI-PRICAI 3D AI 2020挑战:例分解中赢得第二位。