Shypd.aiThe largest open index of AI toolsddpm-segmentation is an open-source tool for label-efficient semantic segmentation using diffusion models. It provides implementations for various models and datasets, outperforming alternatives in few-shot scenarios.
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ddpm-segmentation is an official implementation of the paper "Label-Efficient Semantic Segmentation with Diffusion Models" (ICLR'2022). This open-source project investigates representations learned by state-of-the-art Denoising Diffusion Probabilistic Models (DDPMs) and demonstrates their value for downstream vision tasks. The tool offers a simple semantic segmentation approach that leverages these representations, showing superior performance in few-shot operating points compared to other methods. It includes implementations for DDPM, DatasetDDPM, MAE, SwAV, and DatasetGAN, along with pretrained models and scripts for training interpreters and generating synthetic datasets. The project is built upon datasetGAN and guided-diffusion techniques, providing a robust framework for research and application in semantic segmentation.
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