Partially-supervised instance segmentation is a task which requests
segmenting objects from novel unseen categories via learning on limited seen
categories with annotated masks thus eliminating demands of heavy annotation
burden. The key to addressing this task is to build an effective class-agnostic
mask segmentation model. Unlike previous methods that learn such models only on
seen categories, in this paper, we propose a new method, named ContrastMask,
which learns a mask segmentation model on both seen and unseen categories under
a unified pixel-level contrastive learning framework. In this framework,
annotated masks of seen categories and pseudo masks of unseen categories serve
as a prior for contrastive learning, where features from the mask regions
(foreground) are pulled together, and are contrasted against those from the
background, and vice versa. Through this framework, feature discrimination
between foreground and background is largely improved, facilitating learning of
the class-agnostic mask segmentation model. Exhaustive experiments on the COCO
dataset demonstrate the superiority of our method, which outperforms previous
state-of-the-arts.

通过在受限的可见类别上学习有标注遮罩，从而消除繁重注释负担，并在新颖的 unseen 类别上分割对象，部分监督实例分割是一个任务。本文提出了 ContrastMask，它在统一的像素级对比学习框架下，同时在可见和未知类别上学习掩模分割模型，提高了前景和背景的特征辨别率，促进了课程无关掩模分割模型的学习，在 COCO 数据集上的详尽实验证明了我们方法的优越性，胜过了之前的最新技术。