论文标题
第二名解决方案，以实例分段IJCAI 3D AI挑战2020
2nd Place Solution to Instance Segmentation of IJCAI 3D AI Challenge 2020
论文作者
论文摘要
与MS-Coco相比，竞争的数据集具有较大比例的大于96x96像素的区域。由于获得良好的边界对于大型对象分割至关重要，因此选择带有PoIntrend的蒙版R-CNN作为输出高质量对象边界的基本分割框架。此外，使用Resnest，FPN和DCNV2的更好的发动机以及包括多规模培训和测试时间增加在内的一系列有效技巧可用于提高细分性能。我们的最佳性能是由四个型号（三个基于Pointrend的型号和SOLOV2）组成的合奏，该模型赢得了IJCAI-PRICAI 3D AI挑战2020：实例细分的第二名。
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.
