  Semantic segmentation is essential in computer vision for various
applications, yet traditional approaches face significant challenges, including
the high cost of annotation and extensive training for supervised learning.
Additionally, due to the limited predefined categories in supervised learning,
models typically struggle with infrequent classes and are unable to predict
novel classes. To address these limitations, we propose MaskDiffusion, an
innovative approach that leverages pretrained frozen Stable Diffusion to
achieve open-vocabulary semantic segmentation without the need for additional
training or annotation, leading to improved performance compared to similar
methods. We also demonstrate the superior performance of MaskDiffusion in
handling open vocabularies, including fine-grained and proper noun-based
categories, thus expanding the scope of segmentation applications. Overall, our
MaskDiffusion shows significant qualitative and quantitative improvements in
contrast to other comparable unsupervised segmentation methods, i.e. on the
Potsdam dataset (+10.5 mIoU compared to GEM) and COCO-Stuff (+14.8 mIoU
compared to DiffSeg). All code and data will be released at
https://github.com/Valkyrja3607/MaskDiffusion.
