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arXiv · 2112.00390

SegDiff: Image Segmentation with Diffusion Probabilistic Models

Tomer Amit·Tal Shaharbany·Eliya Nachmani·Lior Wolf
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Abstract

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.

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Tomer Amit, Tal Shaharbany, Eliya Nachmani, Lior Wolf. 2022-09-07. SegDiff: Image Segmentation with Diffusion Probabilistic Models. https://arxiv.org/abs/2112.00390

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