We demonstrate our solution for the 2019 COCO panoptic segmentation task. Our method first performs instance segmentation and semantic segmentation separately, then combines the two to generate panoptic segmentation results. To enhance the performance, we add several expert models of Mask R-CNN in instance segmentation to tackle the data imbalance problem in the training data; also HTC model is adopted yielding our best instance segmentation results. In semantic segmentation, we trained several models with various backbones and use an ensemble strategy which further boosts the segmentation results. In the end, we analyze various combinations of instance and semantic segmentation, and report on their performance for the final panoptic segmentation results. Our best model achieves $PQ$ 47.1 on 2019 COCO panoptic test-dev data.

翻译：我们展示了针对2019年COCO全景分割任务的解决方案。该方法首先分别执行实例分割和语义分割，随后将两者结果融合生成全景分割结果。为提升性能，我们在实例分割中引入了多个Mask R-CNN专家模型，以应对训练数据中的类别不平衡问题；同时采用HTC模型获得了最优的实例分割结果。在语义分割方面，我们训练了多个采用不同骨干网络的模型，并通过集成策略进一步提升了分割效果。最终，我们分析了实例分割与语义分割的多种组合方式，并报告了其对全景分割最终结果的影响。最佳模型在2019年COCO全景分割测试开发数据集上取得了$PQ$ 47.1的评分。