SALAD通过建模物体组合分布，精准识别逻辑异常。
SALAD -- Semantics-Aware Logical Anomaly Detection
- 引入新组合分支，显式建模物体组合图分布
- 在MVTec LOCO上实现96.1%图像级AUROC
- 无需人工标注或类别信息，适合工业质检场景
近期表面异常检测方法在识别结构异常（如凹陷、划痕）方面表现优异，但难以发现逻辑异常（如物体组件缺失或不规则）。现有最优逻辑异常检测方法依赖聚合的预训练特征或手工设计描述符（多来自组合图），丢失了空间与语义信息，导致性能受限。本文提出SALAD，一种语义感知的判别式逻辑异常检测方法，新增组合分支以显式建模物体组合图分布，从而学习关键语义关系。同时，提出一种新型组合图提取流程，无需人工标签或类别特异性信息。通过有效建模组合图分布，SALAD在标准逻辑异常检测基准MVTec LOCO上显著超越当前最优方法，图像级AUROC达96.1%。代码已开源。
原文摘要 · Abstract (English)
Recent surface anomaly detection methods excel at identifying structural anomalies, such as dents and scratches, but struggle with logical anomalies, such as irregular or missing object components. The best-performing logical anomaly detection approaches rely on aggregated pretrained features or handcrafted descriptors (most often derived from composition maps), which discard spatial and semantic information, leading to suboptimal performance. We propose SALAD, a semantics-aware discriminative logical anomaly detection method that incorporates a newly proposed composition branch to explicitly model the distribution of object composition maps, consequently learning important semantic relationships. Additionally, we introduce a novel procedure for extracting composition maps that requires no hand-made labels or category-specific information, in contrast to previous methods. By effectively modelling the composition map distribution, SALAD significantly improves upon state-of-the-art methods on the standard benchmark for logical anomaly detection, MVTec LOCO, achieving an impressive image-level AUROC of 96.1%. Code: https://github.com/MaticFuc/SALAD
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