Abstract
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.
Abstract (translated)
提出了一种以图像级类标签为监督的实例分割学习方法。我们的方法生成训练图像的伪实例分割标签，用于训练一个完全监督的模型。为了生成伪标签，我们首先从一个图像分类模型的注意力图中识别出对象类的自信种子区域，然后传播它们以发现具有精确边界的整个实例区域。为此，我们提出了IRNET，它估计单个实例的大致区域，并检测不同对象类之间的边界。因此，它可以将实例标签分配给种子，并在边界内传播它们，以便准确估计实例的整个区域。此外，IRnet在注意力图上接受了像素间关系的训练，因此不需要额外的监督。我们使用IRNET的方法在Pascal VOC 2012数据集上取得了卓越的性能，不仅超过了以前接受过相同监管水平培训的最先进水平，而且也超过了以前依靠更强大监管的一些模型。
URL
https://arxiv.org/abs/1904.05044
