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Concept Bottleneck Models Enhance Explainable Visual Anomaly Detection

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a novel framework for explainable visual anomaly detection (VAD) by extending Concept Bottleneck Models (CBMs). This approach aims to provide more semantically meaningful interpretations of anomalies by learning human-understandable concepts. The proposed dual-branch architecture combines a CBM-based concept branch with a visual branch for pixel-level localization, bridging semantic and spatial interpretability. Evaluations on established benchmarks show comparable performance to existing VAD methods while offering richer, concept-driven explanations, with a user study indicating improved effectiveness and reduced misleading explanations compared to a state-of-the-art VLM-based explainer. AI

IMPACT Enhances interpretability in visual anomaly detection, potentially improving human-AI collaboration in quality control and diagnostics.

RANK_REASON Academic paper detailing a new method for visual anomaly detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Concept Bottleneck Models Enhance Explainable Visual Anomaly Detection

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Academic paper detailing a new method for visual anomaly detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    Explainable Visual Anomaly Detection via Concept Bottleneck Models

    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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