Producing quality segmentation masks for images is a fundamental problem in
computer vision. Recent research has explored large-scale supervised training
to enable zero-shot segmentation on virtually any image style and unsupervised
training to enable segmentation without dense annotations. However,
constructing a model capable of segmenting anything in a zero-shot manner
without any annotations is still challenging. In this paper, we propose to
utilize the self-attention layers in stable diffusion models to achieve this
goal because the pre-trained stable diffusion model has learned inherent
concepts of objects within its attention layers. Specifically, we introduce a
simple yet effective iterative merging process based on measuring KL divergence
among attention maps to merge them into valid segmentation masks. The proposed
method does not require any training or language dependency to extract quality
segmentation for any images. On COCO-Stuff-27, our method surpasses the prior
unsupervised zero-shot SOTA method by an absolute 26% in pixel accuracy and 17%
in mean IoU.

利用稳定扩散模型中的自注意力层以及基于测量注意力图之间的 KL 散度的简单而有效的迭代合并过程，我们提出了一种无需训练或语言依赖性，能够从任何图像中提取质量分割的方法。在 COCO-Stuff-27 上，我们的方法在像素准确率和平均 IoU 上都比之前最先进的无监督零样本方法提高了 26% 和 17%。