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Related Experiment Video

Updated: May 14, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Semantic-Guided Multi-Level Collaborative Fusion Network for Visible and Infrared Images.

Lijun Yuan¹, Chuanjiang Xie¹, Ming Yang^1,2,3

¹College of Aviation Electronics and Electrical, Civil Aviation Flight University of China, Guanghan 618307, China.

Sensors (Basel, Switzerland)

|May 13, 2026

Summary

This summary is machine-generated.

This study introduces DSIFuse, a novel semantic-guided network for image fusion. DSIFuse enhances downstream task performance by generating semantically rich fusion representations from visible and infrared images.

Keywords:

cross-attention transformer cross-modal fusion feature calibration multi-level interaction object detection semantic prior

Related Experiment Videos

Last Updated: May 14, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Image fusion enhances downstream tasks but current methods lack semantic richness.
Semantic deficiency in fusion representations limits compatibility with subsequent applications.

Purpose of the Study:

To propose DSIFuse, a semantic-guided multi-level collaborative fusion network.
To improve the semantic quality of fused images for better downstream task performance.

Main Methods:

Leveraging semantic priors and global context via auxiliary segmentation branches.
Employing a cross-modal feature correction mechanism for semantic alignment.
Utilizing a three-level interaction strategy and semantic compensation block for enriched representations.

Main Results:

DSIFuse generates clear fusion images with improved structural consistency and reduced artifacts.
Fused representations from DSIFuse enhance performance in downstream object detection tasks.
Experimental validation on public datasets confirms the effectiveness of the proposed network.

Conclusions:

DSIFuse effectively addresses the semantic deficiency in image fusion.
The proposed network significantly improves downstream task performance through semantically enriched fusion.
DSIFuse offers a promising approach for visible-infrared image fusion applications.