SegDiff: Image Segmentation with Diffusion Probabilistic Models
This addresses image segmentation for computer vision applications, representing an incremental advancement by adapting existing diffusion methods.
The paper tackles image segmentation by extending diffusion probabilistic models to this task, achieving state-of-the-art results on datasets like Cityscapes, Vaihingen, and MoNuSeg.
Diffusion Probabilistic Methods are employed for state-of-the-art image generation. In this work, we present a method for extending such models for performing image segmentation. The method learns end-to-end, without relying on a pre-trained backbone. The information in the input image and in the current estimation of the segmentation map is merged by summing the output of two encoders. Additional encoding layers and a decoder are then used to iteratively refine the segmentation map, using a diffusion model. Since the diffusion model is probabilistic, it is applied multiple times, and the results are merged into a final segmentation map. The new method produces state-of-the-art results on the Cityscapes validation set, the Vaihingen building segmentation benchmark, and the MoNuSeg dataset.