Seesaw Loss for Long-Tailed Instance Segmentation
Seesaw Loss for Long-Tailed Instance Segmentation
Instance segmentation has witnessed a remarkable progress on class-balanced benchmarks. However, they fail to perform as accurately in real-world scenarios, where the category distribution of objects naturally comes with a long tail. Instances of head classes dominate a long-tailed dataset and they serve as negative samples of tail categories. The …