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Label-Efficient Semantic Segmentation with Diffusion Models (2112.03126v3)

Published 6 Dec 2021 in cs.CV and cs.LG

Abstract: Denoising diffusion probabilistic models have recently received much research attention since they outperform alternative approaches, such as GANs, and currently provide state-of-the-art generative performance. The superior performance of diffusion models has made them an appealing tool in several applications, including inpainting, super-resolution, and semantic editing. In this paper, we demonstrate that diffusion models can also serve as an instrument for semantic segmentation, especially in the setup when labeled data is scarce. In particular, for several pretrained diffusion models, we investigate the intermediate activations from the networks that perform the Markov step of the reverse diffusion process. We show that these activations effectively capture the semantic information from an input image and appear to be excellent pixel-level representations for the segmentation problem. Based on these observations, we describe a simple segmentation method, which can work even if only a few training images are provided. Our approach significantly outperforms the existing alternatives on several datasets for the same amount of human supervision.

Citations (434)

Summary

  • The paper shows that DDPMs can learn high-level semantic representations from intermediate network activations for effective segmentation.
  • It proposes a simple segmentation approach based on U-Net activation analysis during the reverse diffusion process, outperforming traditional GAN-based methods.
  • Empirical results on datasets like LSUN and FFHQ reveal higher mIoU scores and enhanced robustness in few-shot learning scenarios.

Insights into Label-Efficient Semantic Segmentation with Diffusion Models

The paper "Label-Efficient Semantic Segmentation with Diffusion Models" presents a compelling exploration of how denoising diffusion probabilistic models (DDPM) can be repurposed for semantic segmentation, especially in contexts where labeled data is sparse. Employing diffusion models in generative tasks, such as image synthesis and enhancement, has been extensively documented. This research broadens their utility by demonstrating that these models can effectively act as representation learners for segmentation tasks.

Methodology and Core Contributions

The authors embark on a rigorous analysis of intermediate activations from U-Net networks within DDPMs to ascertain their viability in capturing semantic information at the pixel level. They propose a straightforward segmentation technique that leverages pretrained diffusion models' ability to encapsulate semantic features during the intermediate stages of the reverse diffusion process. Through this method, they develop a semantic segmentation framework that significantly surpasses existing approaches in few-shot learning contexts, where labeled images are limited.

Three main contributions are highlighted:

  1. Semantic Representation via DDPMs: A detailed investigation into how DDPMs serve as representation learners demonstrates their capacity to capture high-level semantic information for complex vision tasks.
  2. Development of an Efficient Segmentation Approach: The research outlines an effective segmentation strategy built on DDPM-derived representations, showcasing superior performance in label-scarce scenarios compared to contemporaneous methods.
  3. Comparison with GAN-based Representations: The paper provides a comparative analysis between DDPM and GAN-derived representations on identical datasets, underscoring the superiority of DDPM in semantic segmentation.

Empirical Results and Robustness

In extensive experiments across various datasets, including LSUN categories and FFHQ, the proposed DDPM-based method consistently outperforms GAN-centric methods, such as DatasetGAN, both qualitatively and quantitatively in terms of mean Intersection over Union (mIoU). This outperformance is attributed to the more informative and semantically coherent features extracted from DDPMs, which present less domain discrepancy between synthetic and real images compared to GANs.

Moreover, a critical insight from the analysis reveals that the middle layers of the reverse diffusion process within DDPMs harbor the most semantically rich features. This finding influences the methodological choice of features and timesteps used for effective segmentation.

Implications and Future Directions

The paper posits far-reaching implications for the field of computer vision, advocating for a paradigm shift towards leveraging diffusion models as foundational components in segmented representation learning tasks. The superior label efficiency, combined with robustness to various image distortions, paints a promising trajectory for the adaptation of diffusion models in cases where labeled data is constrained.

However, a noted limitation is the computational demand and training complexity of high-quality diffusion models on intricate datasets like ImageNet. Despite this, the authors remain optimistic, suggesting that rapid advancements in diffusion model architectures could soon mitigate these challenges, offering broader applicability and enhanced performance.

Concluding Thoughts

This paper substantially contributes to the discourse on unsupervised and semi-supervised learning methodologies within computer vision, particularly emphasizing the role of novel generative models like DDPMs. By successfully positioning diffusion models within the toolkit of representation learning for segmentation, it sets the stage for future explorations into similar high-dimensional generative frameworks and their application across previously unexplored domains. The work calls for continued research to refine and expand the capabilities of diffusion models, potentially unveiling new avenues for their deployment in resource-constrained environments.