SegDiff: Image Segmentation with Diffusion Probabilistic Models (2112.00390v3)

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.

• The paper introduces SegDiff, a novel diffusion model that iteratively refines segmentation maps using an end-to-end training process.
• It employs encoder fusion to integrate both raw images and segmentation estimates without relying on pre-trained backbones.
• It demonstrates superior performance on benchmarks like Cityscapes and Vaihingen, highlighting its efficiency with limited training data.

Image Segmentation with Diffusion Probabilistic Models: Insights and Implications

The paper "SegDiff: Image Segmentation with Diffusion Probabilistic Models" explores the application of diffusion probabilistic models, predominantly used for state-of-the-art image generation, in the context of image segmentation tasks. This novel approach extends the capabilities of diffusion models beyond generative tasks, typically associated with diverse outputs. Instead, it tackles the segmentation challenge where a definitive ground truth exists.

Methodology

The segmentation method introduced, SegDiff, is distinct in its utilization of a diffusion model, not relying on pre-trained backbones, but instead implementing an end-to-end learning process. It achieves segmentation by iteratively refining segmentation maps, combining two encoding pathways for both input images and segmentation estimations.

Key to this approach is the probabilistic nature of diffusion models, allowing multiple applications and offering the potential for aggregating results from multiple inferences into a cohesive segmentation map. The method proposes several architectural innovations:

1. End-to-End Learning: Unlike traditional segmentation models that utilize pre-trained networks, SegDiff is trained from scratch. This facilitates learning specifically tailored to the segmentation task.
2. Encoder Fusion: Information from the input image and the ongoing segmentation estimate are cumulatively encoded and fed into a U-Net architecture for refinement, a departure from standard concatenation strategies utilized in diffusion tasks for conditioning.
3. Multiple Outputs Aggregation: The stochastic nature of the diffusion process generates diverse segmentation results across multiple inference runs, later averaged for enhanced accuracy.

Results

SegDiff demonstrates superior performance across varied benchmarks, achieving state-of-the-art results on the Cityscapes validation set, the Vaihingen building segmentation benchmark, and the MoNuSeg dataset. Notably, the method excels particularly on smaller datasets, suggesting an efficiency in learning from limited samples that can be crucial in niche applications.

Theoretical and Practical Implications

This research signifies a substantial step toward leveraging diffusion probabilistic models for deterministic tasks such as image segmentation. The end-to-end training regime defies the convention of transfer learning domination, demonstrating that diffusion models can learn effectively without pre-trained weights.

Practically, SegDiff’s performance on benchmarks with limited training data highlights its potential utility in specialized domains like medical imaging, where annotated data is sparse.

Future Directions

Future research could explore several avenues based on these findings:

• Enhanced Scalability: Investigating methods to reduce computational overhead while maintaining performance excellence could make SegDiff viable for broader real-time applications.
• Few-Shot Learning: Analyzing how diffusion models might be adapted or further optimized for few-shot learning scenarios, given SegDiff's robustness with small datasets.
• Cross-Domain Transfers: While SegDiff shows promise without pre-training, future work might delve into strategies for cross-domain transfer to harness benefits from both end-to-end and transfer learning paradigms.

Conclusion

The SegDiff framework not only extends the landscape of diffusion models into the field of deterministic problems but also pushes the boundaries of image segmentation capabilities, underscoring the adaptability and potential of diffusion probabilistic methods. This paper warrants further investigation and development into versatile, adaptable AI solutions for complex vision tasks.