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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation.

Lukas Hoyer, Dengxin Dai, Luc Van Gool

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 28, 2023

Summary

This summary is machine-generated.

This study introduces DAFormer and HRDA, novel networks for unsupervised domain adaptation and generalization in semantic segmentation. These methods significantly enhance model performance on unseen data by addressing common domain biases and improving context capture.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Unsupervised domain adaptation (UDA) and domain generalization (DG) are crucial for applying machine learning models to new, unlabeled datasets.
Existing UDA and DG semantic segmentation methods often rely on outdated network architectures.
Challenges include overfitting to source domains and preserving fine details or long-range context due to image manipulation for memory efficiency.

Purpose of the Study:

To benchmark recent network architectures, including Transformers, for UDA and DG semantic segmentation.
To design and introduce the DAFormer network tailored for UDA and DG tasks.
To propose HRDA, a multi-resolution framework to overcome limitations of low-resolution or cropped images in UDA/DG.

Main Methods:

Benchmarking recent architectures like Transformers for UDA/DG.
Developing DAFormer with three strategies: Rare Class Sampling, Thing-Class ImageNet Feature Distance, and learning rate warmup.
Introducing HRDA, a multi-resolution framework using high-resolution crops for details and low-resolution crops for context with learned scale attention.

Main Results:

DAFormer and HRDA significantly improve state-of-the-art performance in UDA and DG semantic segmentation.
Performance gains exceed 10 mIoU across 5 diverse benchmarks.
The proposed methods effectively mitigate source domain bias and enhance context understanding.

Conclusions:

DAFormer and HRDA represent significant advancements in UDA and DG for semantic segmentation.
The developed strategies effectively address overfitting and memory constraints.
The multi-resolution approach in HRDA successfully balances detail preservation and context capture for robust domain generalization.