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SegDiff: Image Segmentation with Diffusion Probabilistic Models

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arxiv 2112.00390 v3 pith:DVN2QY6B submitted 2021-12-01 cs.CV cs.AIcs.LG

Tomer Amit , Tal Shaharbany , Eliya Nachmani , Lior Wolf This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords segmentationdiffusionimagemethodprobabilisticmergedmodelmodels
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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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Cited by 36 Pith papers

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