Denoising diffusion probabilistic models have recently received much research
attention since they outperform alternative approaches, such as GANs, and
currently provide state-of-the-art generative performance. The superior
performance of diffusion models has made them an appealing tool in several
applications, including inpainting, super-resolution, and semantic editing. In
this paper, we demonstrate that diffusion models can also serve as an
instrument for semantic segmentation, especially in the setup when labeled data
is scarce. In particular, for several pretrained diffusion models, we
investigate the intermediate activations from the networks that perform the
Markov step of the reverse diffusion process. We show that these activations
effectively capture the semantic information from an input image and appear to
be excellent pixel-level representations for the segmentation problem. Based on
these observations, we describe a simple segmentation method, which can work
even if only a few training images are provided. Our approach significantly
outperforms the existing alternatives on several datasets for the same amount
of human supervision.

本文研究了扰动扩散概率模型在语义分割任务中的应用，特别是在标注数据有限的情况下。通过探究预训练扩散模型的中间层，我们发现它们可以有效地捕捉输入图像的语义信息，并成为像素级别的分割表示。基于这些观察，我们提出了一种简单的分割方法，能在仅有少量训练图像的情况下显著提高性能。