Label-Efficient Semantic Segmentation with Diffusion Models

Understand

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.