  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 overwhelming gradients of
negative samples on tail classes lead to a biased learning process for
classifiers. Consequently, objects of tail categories are more likely to be
misclassified as backgrounds or head categories. To tackle this problem, we
propose Seesaw Loss to dynamically re-balance gradients of positive and
negative samples for each category, with two complementary factors, i.e.,
mitigation factor and compensation factor. The mitigation factor reduces
punishments to tail categories w.r.t. the ratio of cumulative training
instances between different categories. Meanwhile, the compensation factor
increases the penalty of misclassified instances to avoid false positives of
tail categories. We conduct extensive experiments on Seesaw Loss with
mainstream frameworks and different data sampling strategies. With a simple
end-to-end training pipeline, Seesaw Loss obtains significant gains over
Cross-Entropy Loss, and achieves state-of-the-art performance on LVIS dataset
without bells and whistles. Code is available at
https://github.com/open-mmlab/mmdetection.
