FreqAnchorAD: Language-Free Zero-Shot Anomaly Detection via Frequency-Deviation Anchoring

2026-08-01

AI Summary

This work addresses the limitations of existing zero-shot anomaly detection methods, which rely solely on spatial features and struggle to capture subtle textural and structural deviations—particularly in the absence of target-domain data. To overcome this, we propose the first frequency-aware zero-shot anomaly detection framework, introducing a novel local spatial-frequency discrepancy modeling approach. Our method employs a Local Frequency Compensation Module (LFCM), a Frequency Discrepancy Anchor Projector (FDAP), and Asymmetric Anchor Supervision (AAS) to construct normal and anomalous frequency-domain anchors for relative similarity discrimination. Notably, it achieves language-agnostic detection without requiring textual prompts. Extensive experiments demonstrate that our approach sets new state-of-the-art average performance across 13 industrial and medical benchmarks, excelling in both image-level recognition and pixel-level localization tasks.

Abstract

Zero-shot anomaly detection (ZSAD) aims to detect anomalies and localize defective regions in unseen target domains without target training data. Recent ZSAD methods build on pretrained vision models, particularly CLIP, and construct normal and anomaly references from textual prompts or learnable visual representations. These methods perform anomaly discrimination primarily in spatial feature spaces, where subtle changes in texture, boundaries, and local structures can be confused with normal appearance variations. Although inconspicuous spatially, such defects can disrupt local texture regularity or boundary continuity, inducing response deviations across frequency bands. However, existing ZSAD methods do not explicitly model these frequency-dependent characteristics. Our image-domain analysis reveals that local defects exhibit spatial-frequency deviations from normal references across low-, middle-, and high-frequency bands, indicating that anomaly evidence is not universally dominated by high-frequency responses. Motivated by this observation, we propose FreqAnchorAD, a frequency-aware framework that organizes frequency-enhanced responses for anchor-relative anomaly discrimination. Specifically, the Local Frequency Compensation Module (LFCM) enhances intermediate patch tokens with local spatial-frequency cues. The Frequency-Deviation Anchor Projector (FDAP), our core discrimination module, organizes enhanced responses along a source-derived channel coordinate and measures anomaly evidence through relative similarity to normal and anomaly anchors. Finally, Asymmetric Anchor Supervision (AAS) stabilizes normal-anchor alignment while preserving diverse anomaly patterns. Experiments on thirteen industrial and medical benchmarks show that FreqAnchorAD achieves state-of-the-art mean performance in image-level anomaly recognition and pixel-level defect localization.

Problem

• zero-shot anomaly detection
• frequency deviation
• spatial-frequency analysis
• anomaly localization
• pretrained vision models

Innovation

• frequency-aware anomaly detection
• zero-shot learning
• spatial-frequency deviation
• anchor-relative discrimination
• CLIP-based anomaly detection