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Related Experiment Video

Updated: Jul 4, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Location Matters: Frequency-Spatial Dual-Space Adaptation for Cross-Domain Few-Shot Segmentation.

Jingyi Zhang, Guolei Sun, Yong Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 2, 2026
    PubMed
    Summary

    This study introduces a novel framework for cross-domain few-shot semantic segmentation, leveraging spatial correspondence to improve model performance. The frequency-spatial dual space adaptation (FDSA) method enhances structural understanding for better segmentation results.

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    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Cross-domain few-shot semantic segmentation (CD-FSS) methods often neglect the inherent spatial correspondence between images.
    • This spatial correlation, crucial for understanding object structure across domains, is driven by consistent foreground object layouts.

    Purpose of the Study:

    • To propose a novel frequency-spatial dual space adaptation (FDSA) framework for CD-FSS.
    • To exploit the domain-agnostic spatial correspondence prior for improved segmentation accuracy.
    • To learn domain-invariant structures and task-specific priors by integrating frequency and spatial domain adaptations.

    Main Methods:

    • The proposed FDSA framework comprises two modules: Frequency Structural Adapter (FSA) and Spatial Geometry Adapter (SGA).
    • FSA modulates images in the frequency domain to preserve structural integrity by emphasizing low-frequency semantics and reducing high-frequency noise.

    Related Experiment Videos

    Last Updated: Jul 4, 2026

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

  • SGA utilizes local descriptors to extract keypoints and generate Gaussian-based geometric priors for aligning regions, further enhanced by spatial-guided SAM refinement (SSR) for mask refinement.

    • Main Results:

    • The FDSA framework achieves state-of-the-art performance on four standard CD-FSS benchmarks.
    • The integration of frequency and spatial domain adaptations effectively captures domain-invariant structures and task-specific priors.
    • Spatial-guided SAM refinement (SSR) enables high-quality segmentation mask refinement without manual intervention.

    Conclusions:

    • The proposed FDSA framework successfully addresses the limitations of existing CD-FSS methods by exploiting spatial correspondence.
    • The dual-space adaptation approach significantly improves the learning of domain-invariant structures and task-specific priors.
    • The method demonstrates superior performance and provides a robust solution for cross-domain few-shot semantic segmentation tasks.