This paper presents a novel approach for learning instance segmentation with image-level class labels as supervision. Our approach generates pseudo instance segmentation labels of training images, which are used to train a fully supervised model. For generating the pseudo labels, we first identify confident seed areas of object classes from attention maps of an image classification model, and propagate them to discover the entire instance areas with accurate boundaries. To this end, we propose IRNet, which estimates rough areas of individual instances and detects boundaries between different object classes. It thus enables to assign instance labels to the seeds and to propagate them within the boundaries so that the entire areas of instances can be estimated accurately. Furthermore, IRNet is trained with inter-pixel relations on the attention maps, thus no extra supervision is required. Our method with IRNet achieves an outstanding performance on the PASCAL VOC 2012 dataset, surpassing not only previous state-of-the-art trained with the same level of supervision, but also some of previous models relying on stronger supervision.

本文提出了一种新颖的方法，使用图像级别的类标签作为监督来学习实例分割。我们的方法生成训练图像的伪实例分割标签，并用于训练完全监督模型。通过IRNet，我们首先从图像分类模型的attention maps中识别出与对象类别相关的种子区域，然后传播到整个实例区域，估计出准确的边界，并为种子分配实例标签，使得整个实例区域可以准确地估计。IRNet是基于attention maps的像素间关系训练的，因此不需要额外的监督。在PASCAL VOC 2012数据集上，我们的方法与IRNet取得了出色的表现，不仅超过了之前使用相同级别监督训练的最先进模型，还超过了一些依赖于更强监督的先前模型。

利用像素间关系进行实例分割的弱监督学习