HarmoniAD: Harmonizing Local Structures and Global Semantics for Anomaly Detection

• URL: http://arxiv.org/abs/2601.00327v1
• Date: Thu, 01 Jan 2026 12:45:45 GMT
• Title: HarmoniAD: Harmonizing Local Structures and Global Semantics for Anomaly Detection
• Authors: Naiqi Zhang, Chuancheng Shi, Jingtong Dou, Wenhua Wu, Fei Shen, Jianhua Cao,
• Abstract summary: Anomaly detection crucial in industrial product quality inspection.<n>Existing methods face a structure-semantics trade-off.<n>HarmoniAD is a frequency-guided dual-branch framework.
• Score: 4.679561335065019
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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