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Updated: May 9, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

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

Published on: December 15, 2023

Evolving classifiers with background suppression transformer for open-set long-tailed class-incremental remote

Yu Song1, Sichao Fu2, Hongquan Xin3

  • 1College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, 266580, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 7, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for remote sensing scene classification that handles imbalanced data and unknown classes. The EC-BST model improves feature learning and reduces confusion between known and unknown classes in challenging scenarios.

Keywords:
Background suppressionClass-incremental learningLong-tail distributionOpen-set recognitionRemote sensing scene classification

Related Experiment Videos

Last Updated: May 9, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

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

Published on: December 15, 2023

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Class-incremental learning (CIL) is crucial for updating remote sensing models without forgetting past knowledge.
  • Real-world remote sensing data presents challenges like long-tail distributions and unknown classes, hindering existing CIL methods.
  • Open-set long-tailed class-incremental remote sensing scene classification (OSLT-CIRSSC) is a more realistic and difficult task.

Purpose of the Study:

  • To address the limitations of current CIL models in OSLT-CIRSSC tasks.
  • To propose a novel framework that enhances feature backbone adaptability and reduces class confusion.
  • To improve the performance of remote sensing scene classification in the presence of long-tail data and unknown classes.

Main Methods:

  • Developed an effective evolving classifier with a background suppression transformer (EC-BST) framework.
  • Introduced a long-tailed Transformer with adaptive background suppression to focus on salient foreground features.
  • Implemented a classifier prediction confidence and uncertainty-based open-set recognition module to distinguish known and unknown classes.
  • Utilized feature fusion from class centroids to enhance feature embedding separability and compactness.

Main Results:

  • The proposed EC-BST framework demonstrated superior performance on OSLT-CIRSSC tasks.
  • Effectively enhanced feature backbone adaptability for long-tailed remote sensing data.
  • Significantly reduced confusion between known and unknown classes.
  • Improved inter-class separability and intra-class compactness of feature embeddings.

Conclusions:

  • The EC-BST framework offers a robust solution for open-set long-tailed class-incremental remote sensing scene classification.
  • The method shows significant improvements over state-of-the-art approaches in challenging real-world scenarios.
  • This work advances the capabilities of CIL in complex remote sensing applications.