  Existing semantic segmentation approaches are often limited by costly
pixel-wise annotations and predefined classes. In this work, we present
CLIP-S$^4$ that leverages self-supervised pixel representation learning and
vision-language models to enable various semantic segmentation tasks (e.g.,
unsupervised, transfer learning, language-driven segmentation) without any
human annotations and unknown class information. We first learn pixel
embeddings with pixel-segment contrastive learning from different augmented
views of images. To further improve the pixel embeddings and enable
language-driven semantic segmentation, we design two types of consistency
guided by vision-language models: 1) embedding consistency, aligning our pixel
embeddings to the joint feature space of a pre-trained vision-language model,
CLIP; and 2) semantic consistency, forcing our model to make the same
predictions as CLIP over a set of carefully designed target classes with both
known and unknown prototypes. Thus, CLIP-S$^4$ enables a new task of class-free
semantic segmentation where no unknown class information is needed during
training. As a result, our approach shows consistent and substantial
performance improvement over four popular benchmarks compared with the
state-of-the-art unsupervised and language-driven semantic segmentation
methods. More importantly, our method outperforms these methods on unknown
class recognition by a large margin.
