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Related Experiment Video

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EKDSC: Long-tailed recognition based on expert knowledge distillation for specific categories.

Yaping Bai1, Jinghua Li1, Dehui Kong1

  • 1Beijing Key Laboratory of Multimedia and Intelligent Software Technology, School of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China; Beijing Institute of Artificial Intelligence, School of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 19, 2025
PubMed
Summary

Expert Knowledge Distillation for Specific Categories (EKDSC) improves long-tail visual recognition by training specialized teacher models. This method enhances tail-class accuracy while preserving head-class performance, outperforming current state-of-the-art methods.

Keywords:
Knowledge distillationLong-tailed recognitionMulti-classifier ensemble

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Long-tail visual recognition suffers from performance disparities between head and tail classes due to imbalanced data distribution.
  • Existing methods often improve tail-class performance at the expense of head-class accuracy.
  • Effectively transferring external knowledge to address this imbalance remains a challenge.

Purpose of the Study:

  • To propose a novel method, Expert Knowledge Distillation for Specific Categories (EKDSC), to address the performance gap in long-tail visual recognition.
  • To improve tail-class recognition accuracy while simultaneously mitigating performance degradation in head classes.
  • To explore the effective transfer of specialized knowledge from a multi-expert teacher model to a student model.

Main Methods:

  • Developed a specialized teacher model with distinct experts for head, mid, and tail classes to ensure focused learning.
  • Implemented a knowledge distillation strategy where each expert teacher model transfers its specialized knowledge to the student model.
  • Evaluated the EKDSC method on various benchmark datasets, including CIFAR-10 LT, CIFAR-100 LT, ImageNet-LT, iNaturalist 2018, and Places-LT.

Main Results:

  • EKDSC significantly enhances the accuracy of tail classes in long-tail visual recognition tasks.
  • The proposed method effectively mitigates the common performance decrease observed in head classes.
  • Achieved state-of-the-art (SOTA) results, outperforming existing methods by 1-5% on multiple benchmark datasets.

Conclusions:

  • EKDSC offers a robust solution for the long-tail visual recognition problem by balancing performance across all classes.
  • The expert-based knowledge distillation approach is effective in transferring specialized knowledge for improved recognition.
  • The method demonstrates strong generalization capabilities across datasets of varying scales.