  In semi-supervised semantic segmentation, a model is trained with a limited
number of labeled images along with a large corpus of unlabeled images to
reduce the high annotation effort. While previous methods are able to learn
good segmentation boundaries, they are prone to confuse classes with similar
visual appearance due to the limited supervision. On the other hand,
vision-language models (VLMs) are able to learn diverse semantic knowledge from
image-caption datasets but produce noisy segmentation due to the image-level
training. In SemiVL, we propose to integrate rich priors from VLM pre-training
into semi-supervised semantic segmentation to learn better semantic decision
boundaries. To adapt the VLM from global to local reasoning, we introduce a
spatial fine-tuning strategy for label-efficient learning. Further, we design a
language-guided decoder to jointly reason over vision and language. Finally, we
propose to handle inherent ambiguities in class labels by providing the model
with language guidance in the form of class definitions. We evaluate SemiVL on
4 semantic segmentation datasets, where it significantly outperforms previous
semi-supervised methods. For instance, SemiVL improves the state-of-the-art by
+13.5 mIoU on COCO with 232 annotated images and by +6.1 mIoU on Pascal VOC
with 92 labels. Project page: https://github.com/google-research/semivl
