  Instance Segmentation is an interesting yet challenging task in computer
vision. In this paper, we conduct a series of refinements with the Hybrid Task
Cascade (HTC) Network, and empirically evaluate their impact on the final model
performance through ablation studies. By taking all the refinements, we achieve
0.47 on the COCO test-dev dataset and 0.47 on the COCO test-challenge dataset.
