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.

翻译：异常检测在工业产品质量检测中至关重要。未能检测微小缺陷往往会导致严重后果。现有方法面临结构-语义权衡问题：面向结构的模型（如基于频率的滤波器）对噪声敏感，而面向语义的模型（如基于CLIP的编码器）常忽略精细细节。为解决此问题，我们提出HarmoniAD，一种频率引导的双分支框架。特征首先通过CLIP图像编码器提取，随后转换至频域，最终解耦为高频与低频路径，分别对结构与语义进行互补建模。高频分支配备细粒度结构注意力模块（FSAM），以增强纹理与边缘信息来检测微小异常；低频分支采用全局结构上下文模块（GSCM），以捕获长程依赖并保持语义一致性。两个分支共同实现了精细细节与全局语义的平衡。HarmoniAD进一步采用多类别联合训练策略，在MVTec-AD、VisA与BTAD数据集上的实验表明，该方法在敏感性与鲁棒性方面均达到最先进的性能水平。