mlsanityData to 14 Oct 2026

Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization

arXiv 2509.05034 ↗First posted 5 Sep 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

Industrial product inspection is often performed using Anomaly Detection (AD) frameworks trained solely on non-defective samples. Although defective samples can be collected during production, leveraging them usually requires pixel-level annotations, limiting scalability. To address this, we propose ADClick, an Interactive Image Segmentation (IIS) algorithm for industrial anomaly detection. ADClick generates pixel-wise anomaly annotations from only a few user clicks and a brief textual description, enabling precise and efficient labeling that significantly improves AD model performance (e.g., AP = 96.1% on MVTec AD). We further introduce ADClick-Seg, a cross-modal framework that aligns visual features and textual prompts via a prototype-based approach for anomaly detection and localization. By combining pixel-level priors with language-guided cues, ADClick-Seg achieves state-of-the-art results on the challenging ``Multi-class'' AD task (AP = 80.0%, PRO = 97.5%, Pixel-AUROC = 99.1% on MVTec AD).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 52 of its 70 references that are in the map. First posted on arXiv 5 Sep 2025.

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References · 52

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38 more
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