HarmoniAD: Harmonizing Local Structures and Global Semantics for Anomaly Detection

TL;DR

HarmoniAD introduces a dual-branch framework that combines frequency domain analysis with semantic encoding to improve anomaly detection by balancing fine details and global context.

Contribution

The paper proposes HarmoniAD, a novel frequency-guided dual-branch model that effectively integrates structure and semantics for enhanced anomaly detection performance.

Findings

Achieves state-of-the-art results on MVTec-AD, VisA, and BTAD datasets.

Effectively detects tiny defects with high sensitivity.

Balances noise sensitivity and detail preservation through frequency-based decoupling.

Abstract

Anomaly detection is crucial in industrial product quality inspection. Failing to detect tiny defects often leads to serious consequences. Existing methods face a structure-semantics trade-off: structure-oriented models (such as frequency-based filters) are noise-sensitive, while semantics-oriented models (such as CLIP-based encoders) often miss fine details. To address this, we propose HarmoniAD, a frequency-guided dual-branch framework. Features are first extracted by the CLIP image encoder, then transformed into the frequency domain, and finally decoupled into high- and low-frequency paths for complementary modeling of structure and semantics. The high-frequency branch is equipped with a fine-grained structural attention module (FSAM) to enhance textures and edges for detecting small anomalies, while the low-frequency branch uses a global structural context module (GSCM) to capture long-range dependencies and preserve semantic consistency. Together, these branches balance fine detail and global semantics. HarmoniAD further adopts a multi-class joint training strategy, and experiments on MVTec-AD, VisA, and BTAD show state-of-the-art performance with both sensitivity and robustness.