  Semantic segmentation is a key computer vision task that has been actively
researched for decades. In recent years, supervised methods have reached
unprecedented accuracy, however they require many pixel-level annotations for
every new class category which is very time-consuming and expensive.
Additionally, the ability of current semantic segmentation networks to handle a
large number of categories is limited. That means that images containing rare
class categories are unlikely to be well segmented by current methods. In this
paper we propose a novel approach for creating semantic segmentation masks for
every object, without the need for training segmentation networks or seeing any
segmentation masks. Our method takes as input the image-level labels of the
class categories present in the image; they can be obtained automatically or
manually. We utilize a vision-language embedding model (specifically CLIP) to
create a rough segmentation map for each class, using model interpretability
methods. We refine the maps using a test-time augmentation technique. The
output of this stage provides pixel-level pseudo-labels, instead of the manual
pixel-level labels required by supervised methods. Given the pseudo-labels, we
utilize single-image segmentation techniques to obtain high-quality output
segmentation masks. Our method is shown quantitatively and qualitatively to
outperform methods that use a similar amount of supervision. Our results are
particularly remarkable for images containing rare categories.
