Most methods for object instance segmentation require all training examples
to be labeled with segmentation masks. This requirement makes it expensive to
annotate new categories and has restricted instance segmentation models to ~100
well-annotated classes. The goal of this paper is to propose a new partially
supervised training paradigm, together with a novel weight transfer function,
that enables training instance segmentation models on a large set of categories
all of which have box annotations, but only a small fraction of which have mask
annotations. These contributions allow us to train Mask R-CNN to detect and
segment 3000 visual concepts using box annotations from the Visual Genome
dataset and mask annotations from the 80 classes in the COCO dataset. We
evaluate our approach in a controlled study on the COCO dataset. This work is a
first step towards instance segmentation models that have broad comprehension
of the visual world.

该研究提出了一种新的部分监督训练范式和权重转移函数，使得可以在仅有少量掩模注释的情况下，使用来自 Visual Genome 数据集的框注释和 COCO 数据集中 80 个类别的掩模注释，训练出可以检测和分割 3000 个视觉概念的 Mask R-CNN 模型，在 COCO 数据集上进行了实验评估。这是实现广泛理解视觉世界的目标实例分割模型的第一步。