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Updated: Jul 17, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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ShuffleTrans: Patch-wise weight shuffle for transparent object segmentation.

Boxiang Zhang1, Zunran Wang2, Yonggen Ling2

  • 1College of Computer Science and Technology, Jilin University, China; Key Laboratory of Symbolic Computation and Knowledge Engineer, Jilin University, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 2, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces ShuffleTrans, a novel method for segmenting transparent objects by enhancing shape recognition. The network effectively integrates global context, improving accuracy on challenging texture-less datasets.

Keywords:
Semantic segmentationTransparent object segmentation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Transparent object segmentation is difficult due to a lack of texture.
  • Existing methods struggle with global shape cues, relying on limited local information.
  • Shape information is critical for segmenting texture-less transparent objects.

Purpose of the Study:

  • To develop an effective method for transparent object segmentation.
  • To improve the recognition of global shape information in segmentation tasks.
  • To enhance the performance of deep learning models on texture-less objects.

Main Methods:

  • A novel Patch-wise Weight Shuffle operation is proposed to integrate global context with dynamic convolution.
  • A new network, ShuffleTrans, is designed incorporating the Patch-wise Weight Shuffle operation.
  • Two auxiliary modules, Boundary and Direction Refinement Module and Channel Attention Enhancement Module, are introduced to aid segmentation.

Main Results:

  • ShuffleTrans demonstrates superior performance in recognizing shape for transparent object segmentation.
  • Experiments on four texture-less and two normal datasets validate the method's effectiveness and generality.
  • The proposed method achieved 74.93% mIoU on the Trans10k v2 test set, outperforming existing approaches.

Conclusions:

  • The ShuffleTrans network effectively addresses the challenge of transparent object segmentation by leveraging global shape cues.
  • The Patch-wise Weight Shuffle operation and auxiliary modules significantly contribute to improved segmentation accuracy.
  • The method shows strong potential for real-world applications requiring accurate segmentation of transparent objects.