  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.
