A Survey on Interpretability in Visual Recognition

• URL: http://arxiv.org/abs/2507.11099v1
• Date: Tue, 15 Jul 2025 08:45:54 GMT
• Title: A Survey on Interpretability in Visual Recognition
• Authors: Qiyang Wan, Chengzhi Gao, Ruiping Wang, Xilin Chen,
• Abstract summary: This paper systematically reviews existing research on the interpretability of visual recognition models.<n>We propose a taxonomy of methods from a human-centered perspective.<n>We aim to organize existing research in this domain and inspire future investigations into the interpretability of visual recognition models.
• Score: 28.577223694381452
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In recent years, visual recognition methods have advanced significantly, finding applications across diverse fields. While researchers seek to understand the mechanisms behind the success of these models, there is also a growing impetus to deploy them in critical areas like autonomous driving and medical diagnostics to better diagnose failures, which promotes the development of interpretability research. This paper systematically reviews existing research on the interpretability of visual recognition models and proposes a taxonomy of methods from a human-centered perspective. The proposed taxonomy categorizes interpretable recognition methods based on Intent, Object, Presentation, and Methodology, thereby establishing a systematic and coherent set of grouping criteria for these XAI methods. Additionally, we summarize the requirements for evaluation metrics and explore new opportunities enabled by recent technologies, such as large multimodal models. We aim to organize existing research in this domain and inspire future investigations into the interpretability of visual recognition models.

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