  Semantic segmentation is a crucial task in computer vision that involves
segmenting images into semantically meaningful regions at the pixel level.
However, existing approaches often rely on expensive human annotations as
supervision for model training, limiting their scalability to large, unlabeled
datasets. To address this challenge, we present ZeroSeg, a novel method that
leverages the existing pretrained vision-language (VL) model (e.g. CLIP) to
train open-vocabulary zero-shot semantic segmentation models. Although acquired
extensive knowledge of visual concepts, it is non-trivial to exploit knowledge
from these VL models to the task of semantic segmentation, as they are usually
trained at an image level. ZeroSeg overcomes this by distilling the visual
concepts learned by VL models into a set of segment tokens, each summarizing a
localized region of the target image. We evaluate ZeroSeg on multiple popular
segmentation benchmarks, including PASCAL VOC 2012, PASCAL Context, and COCO,
in a zero-shot manner (i.e., no training or adaption on target segmentation
datasets). Our approach achieves state-of-the-art performance when compared to
other zero-shot segmentation methods under the same training data, while also
performing competitively compared to strongly supervised methods. Finally, we
also demonstrated the effectiveness of ZeroSeg on open-vocabulary segmentation,
through both human studies and qualitative visualizations.
