  Zero-shot semantic segmentation (ZS3) aims to segment the novel categories
that have not been seen in the training. Existing works formulate ZS3 as a
pixel-level zeroshot classification problem, and transfer semantic knowledge
from seen classes to unseen ones with the help of language models pre-trained
only with texts. While simple, the pixel-level ZS3 formulation shows the
limited capability to integrate vision-language models that are often
pre-trained with image-text pairs and currently demonstrate great potential for
vision tasks. Inspired by the observation that humans often perform
segment-level semantic labeling, we propose to decouple the ZS3 into two
sub-tasks: 1) a classagnostic grouping task to group the pixels into segments.
2) a zero-shot classification task on segments. The former task does not
involve category information and can be directly transferred to group pixels
for unseen classes. The latter task performs at segment-level and provides a
natural way to leverage large-scale vision-language models pre-trained with
image-text pairs (e.g. CLIP) for ZS3. Based on the decoupling formulation, we
propose a simple and effective zero-shot semantic segmentation model, called
ZegFormer, which outperforms the previous methods on ZS3 standard benchmarks by
large margins, e.g., 22 points on the PASCAL VOC and 3 points on the COCO-Stuff
in terms of mIoU for unseen classes. Code will be released at
https://github.com/dingjiansw101/ZegFormer.
