This paper introduces a novel approach to learning instance segmentation using extreme points, i.e., the topmost, leftmost, bottommost, and rightmost points, of each object. These points are readily available in the modern bounding box annotation process while offering strong clues for precise segmentation, and thus allows to improve performance at the same annotation cost with box-supervised methods. Our work considers extreme points as a part of the true instance mask and propagates them to identify potential foreground and background points, which are all together used for training a pseudo label generator. Then pseudo labels given by the generator are in turn used for supervised learning of our final model. On three public benchmarks, our method significantly outperforms existing box-supervised methods, further narrowing the gap with its fully supervised counterpart. In particular, our model generates high-quality masks when a target object is separated into multiple parts, where previous box-supervised methods often fail.

本论文介绍了一种使用极值点（即每个对象的最上面、最左边、最下面和最右边的点）进行实例分割学习的新方法。通过将极值点视为真实实例掩码的一部分并将其传播以识别潜在的前景和背景点，然后将其用于训练伪标签生成器，再利用生成器提供的伪标签进行最终模型的监督学习，我们的方法在三个公共基准上明显优于现有的基于边界框监督方法，进一步缩小与完全监督对照组之间的差距。特别是在目标对象分隔成多个部分的情况下，我们的模型生成高质量的掩码，而之前的基于边界框监督方法经常失败。

极点监督实例分割