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English(EN) Explainable Visual Anomaly Detection via Concept Bottleneck Models

概念瓶颈模型增强了可解释的视觉异常检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员通过扩展概念瓶颈模型（CBMs）引入了一个新的可解释视觉异常检测（VAD）框架。该方法旨在通过学习人类可理解的概念来提供更具语义意义的异常解释。提出的双分支架构结合了基于CBM的概念分支和用于像素级定位的视觉分支，从而弥合了语义和空间可解释性之间的差距。在既定基准上的评估显示，其性能与现有的VAD方法相当，同时提供了更丰富、由概念驱动的解释。一项用户研究表明，与最先进的基于VLM的解释器相比，其有效性有所提高，误导性解释有所减少。 AI

影响 增强了视觉异常检测的可解释性，有望改善质量控制和诊断中的人机协作。

排序理由 关于视觉异常检测新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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概念瓶颈模型增强了可解释的视觉异常检测

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关于视觉异常检测新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Arianna Stropeni, Valentina Zaccaria, Francesco Borsatti, Davide Dalle Pezze, Manuel Barusco, Gian Antonio Susto ·

    基于概念瓶颈模型的视觉异常检测可解释性研究

    arXiv:2511.20088v3 Announce Type: replace-cross Abstract: In recent years, VAD (Visual Anomaly Detection) has attracted significant attention due to its high interpretability, as it provides visual explanations by highlighting anomalous regions within an image. However, although …

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