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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Semantic segmentation feature fusion network based on transformer.

Tianping Li1, Zhaotong Cui1, Hua Zhang2

  • 1School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, China.

Scientific Reports
|February 19, 2025
PubMed
Summary

This study introduces a novel transformer-based semantic segmentation network (FFTNet) combining Convolutional Neural Networks (CNNs) and Transformers. FFTNet enhances global and local feature representation for improved image segmentation accuracy.

Keywords:
AttentionFeature fusionSemantic segmentationTransformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Deep Learning

Background:

  • Convolutional Neural Networks (CNNs) excel at local feature extraction but struggle with global context in image segmentation.
  • Transformers offer global context but can be computationally intensive and may not fully leverage convolutional advantages.
  • Existing methods often lack a balance between capturing local details and global semantic information.

Purpose of the Study:

  • To develop a hybrid deep learning model that integrates CNNs and Transformers for enhanced semantic segmentation.
  • To address the limitations of CNNs in global information acquisition and Transformers in computational efficiency.
  • To improve the accuracy and performance of semantic segmentation by effectively fusing local and global image features.

Main Methods:

  • Introduced a Feature Alignment Module (FAM) to enhance spatial details and channel representations.
  • Utilized a Transformer structure to establish global relationships between pixels, improving pixel representation.
  • Designed a Pyramid Convolutional Pooling Module (PCPM) to compress and enrich feature maps while capturing global correlations, reducing computational load.

Main Results:

  • The proposed transformer-based semantic segmentation feature fusion network (FFTNet) achieved 82.5% mean Intersection over Union (mIoU) on the Cityscapes test dataset.
  • Visualization tests on Pascal VOC 2012 and Cityscapes datasets demonstrated superior performance compared to alternative approaches.
  • The model effectively fused local and global information, leading to improved segmentation accuracy.

Conclusions:

  • The FFTNet model successfully combines the strengths of CNNs and Transformers for robust semantic segmentation.
  • The proposed modules (FAM and PCPM) effectively enhance feature representation and manage computational complexity.
  • This hybrid approach offers a promising direction for advancing semantic segmentation in computer vision.