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<p>Concept-based explanation represents an important yet rapidly evolving method aimed at enhancing the interpretability and transparency of deep learning models by clarifying their behaviors and predictions using understandable concepts. However, the current literature lacks a comprehensive survey and classification of the various strategies and methodologies employed to analyze these models. This paper aims to fill this gap by introducing a new taxonomy of concept-based explanation strategies. Following a thorough review of 101 relevant studies, a preliminary taxonomy was developed that groups strategies based on criteria such as data modality, level of supervision, model complexity, explanation scope, and model interpretability. Furthermore, we present a comprehensive evaluation of the advantages and limitations of various methodologies, as well as the datasets commonly used in this field. We also identify promising avenues for further exploration. Our study aims to serve as a useful tool for researchers and professionals interested in advancing concept-based explanation. Furthermore, we have built a GitHub project page that gathers key materials for concept-based explanations, which may be accessible through : <a href="https://github.com/razanalharith/Concept-Based-Explanation">https://github.com/razanalharith/Concept-Based-Explanation</a>.</p>

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Concept-Based Explanation for Deep Vision Models: A Comprehensive Survey on Techniques, Taxonomy, Applications, and Recent Advances

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Razan Alharith,
  • Jiashu Zhang,
  • Ashraf Osman Ibrahim,
  • Zhenyu Wu

摘要

Concept-based explanation represents an important yet rapidly evolving method aimed at enhancing the interpretability and transparency of deep learning models by clarifying their behaviors and predictions using understandable concepts. However, the current literature lacks a comprehensive survey and classification of the various strategies and methodologies employed to analyze these models. This paper aims to fill this gap by introducing a new taxonomy of concept-based explanation strategies. Following a thorough review of 101 relevant studies, a preliminary taxonomy was developed that groups strategies based on criteria such as data modality, level of supervision, model complexity, explanation scope, and model interpretability. Furthermore, we present a comprehensive evaluation of the advantages and limitations of various methodologies, as well as the datasets commonly used in this field. We also identify promising avenues for further exploration. Our study aims to serve as a useful tool for researchers and professionals interested in advancing concept-based explanation. Furthermore, we have built a GitHub project page that gathers key materials for concept-based explanations, which may be accessible through : https://github.com/razanalharith/Concept-Based-Explanation.