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Related Experiment Video

Updated: Jul 1, 2026

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Semantic-Rearrangement-based Hierarchical Alignment for domain generalized segmentation.

Guanlong Jiao1, Hongqiang Wu2, Chenyangguang Zhang1

  • 1Department of Automation, Tsinghua University, Beijing, 100084, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 16, 2025
PubMed
Summary
This summary is machine-generated.

Domain generalized semantic segmentation models struggle with unseen data. This study introduces Semantic-Rearrangement-based Hierarchical Alignment (SRHA) to create robust, domain-invariant representations by aligning features across local and global levels.

Keywords:
Domain generalizationDomain randomizationRepresentation learningSemantic segmentation

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Domain generalized semantic segmentation aims to train models on source data for effective performance on unseen target domains.
  • Existing methods often rely on global style randomization or feature regularization, which struggle to capture regional visual discrepancies.
  • A key challenge is creating domain-invariant representations that maintain consistency from local to global feature levels.

Purpose of the Study:

  • To propose a novel method, Semantic-Rearrangement-based Hierarchical Alignment (SRHA), to address the limitations of current domain generalization techniques.
  • To enhance the diversity of source domain data through semantic region randomization.
  • To establish consistent domain-invariant representations across multiple feature levels (global, regional, local).

Main Methods:

  • Incorporation of a Semantic Rearrangement Module (SRM) for semantic region randomization, increasing source domain diversity.
  • Introduction of a Hierarchical Alignment Constraint (HAC) to build domain-invariant representations by aligning features across randomized samples.
  • Utilizing domain-neutral knowledge for multi-level feature alignment to bridge the source-target domain gap.

Main Results:

  • SRHA effectively enhances source domain diversity through semantic region randomization.
  • The Hierarchical Alignment Constraint successfully establishes global-regional-local consistent domain-invariant representations.
  • Experimental results demonstrate SRHA's superiority over state-of-the-art methods on various benchmarks.

Conclusions:

  • SRHA offers a more robust approach to handling the domain gap in semantic segmentation by considering regional discrepancies.
  • The proposed method achieves superior performance by aligning features at multiple levels, ensuring consistency from local details to global context.
  • SRHA represents a significant advancement in domain generalized semantic segmentation.