组合实例分割和语义分割用于全景分割 (Ensembling Instance and Semantic Segmentation for Panoptic Segmentation)

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：我们展示了我们针对2019年COCO全景分割任务的解决方案。我们的方法首先分别执行实例分割和语义分割，然后将两者合并以生成全景分割结果。为了增强性能，我们在实例分割中添加了几个专家级别的Mask R-CNN模型，以解决训练数据中存在的数据不平衡问题; 同时，采用HTC模型产生我们最佳的实例分割结果。在语义分割方面，我们针对各种骨干网络训练了几个模型，并使用集成策略进一步提高了分割结果。最后，我们分析了各种实例和语义分割的组合，并报告了它们的全景分割结果的性能。我们的最佳模型在2019年COCO全景测试数据中实现了$PQ$ 47.1。