Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection

TL;DR

This paper introduces RAAD, a novel framework that recalibrates attention in industrial anomaly detection by decomposing and dynamically adjusting attention maps, significantly improving defect detection accuracy and efficiency.

Contribution

It proposes a hierarchical quantization scoring method to dynamically allocate attention focus, reducing bias and enhancing sensitivity in unsupervised industrial anomaly detection.

Findings

RAAD improves detection accuracy across 32 datasets.

Hierarchical quantization reduces computational complexity.

Enhanced focus on defect-prone regions improves sensitivity.

Abstract

Due to the scarcity and unpredictable nature of defect samples, industrial anomaly detection (IAD) predominantly employs unsupervised learning. However, all unsupervised IAD methods face a common challenge: the inherent bias in normal samples, which causes models to focus on variable regions while overlooking potential defects in invariant areas. To effectively overcome this, it is essential to decompose and recalibrate attention, guiding the model to suppress irrelevant variations and concentrate on subtle, defect-susceptible areas. In this paper, we propose Recalibrating Attention of Industrial Anomaly Detection (RAAD), a framework that systematically decomposes and recalibrates attention maps. RAAD employs a two-stage process: first, it reduces attention bias through quantization, and second, it fine-tunes defect-prone regions for improved sensitivity. Central to this framework is Hierarchical Quantization Scoring (HQS), which dynamically allocates bit-widths across layers based on their anomaly detection contributions. HQS dynamically adjusts bit-widths based on the hierarchical nature of attention maps, compressing lower layers that produce coarse and noisy attention while preserving deeper layers with sharper, defect-focused attention. This approach optimizes both computational efficiency and the model' s sensitivity to anomalies. We validate the effectiveness of RAAD on 32 datasets using a single 3090ti. Experiments demonstrate that RAAD, balances the complexity and expressive power of the model, enhancing its anomaly detection capability.