Masked Diffusion: A Self-Supervised Representation Learning Approach for Semantic Segmentation

The proposed masked diffusion model (MDM) is a novel self-supervised pre-training approach that replaces the conventional additive Gaussian noise in denoising diffusion probabilistic models (DDPM) with a masking mechanism, leading to improved performance on downstream semantic segmentation tasks.