Title: Quality-Aware Language-Conditioned Local Auto-Regressive Anomaly Synthesis and Detection Show Chinese title

Title: 质量感知的语言条件局部自回归异常合成与检测

Abstract: Despite substantial progress in anomaly synthesis methods, existing diffusion-based and coarse inpainting pipelines commonly suffer from structural deficiencies such as micro-structural discontinuities, limited semantic controllability, and inefficient generation. To overcome these limitations, we introduce ARAS, a language-conditioned, auto-regressive anomaly synthesis approach that precisely injects local, text-specified defects into normal images via token-anchored latent editing. Leveraging a hard-gated auto-regressive operator and a training-free, context-preserving masked sampling kernel, ARAS significantly enhances defect realism, preserves fine-grained material textures, and provides continuous semantic control over synthesized anomalies. Integrated within our Quality-Aware Re-weighted Anomaly Detection (QARAD) framework, we further propose a dynamic weighting strategy that emphasizes high-quality synthetic samples by computing an image-text similarity score with a dual-encoder model. Extensive experiments across three benchmark datasets-MVTec AD, VisA, and BTAD, demonstrate that our QARAD outperforms SOTA methods in both image- and pixel-level anomaly detection tasks, achieving improved accuracy, robustness, and a 5 times synthesis speedup compared to diffusion-based alternatives. Our complete code and synthesized dataset will be publicly available. Show Chinese abstract

Abstract: 尽管在异常合成方法上取得了显著进展，现有的基于扩散和粗粒度修复的流程通常存在结构上的不足，例如微观结构不连续、有限的语义可控性和生成效率低下。 为了克服这些限制，我们引入了ARAS，这是一种语言条件的自回归异常合成方法，通过标记锚定的潜在编辑精确地将局部的、文本指定的缺陷注入正常图像中。 利用硬门控自回归算子和无训练、保持上下文的掩码采样内核，ARAS显著提高了缺陷的真实性，保留了细粒度的材料纹理，并提供了对合成异常的连续语义控制。 集成在我们的质量感知加权异常检测（QARAD）框架中，我们进一步提出了一种动态加权策略，通过使用双编码器模型计算图像-文本相似性得分来强调高质量的合成样本。 在三个基准数据集-MVTec AD、VisA和BTAD上的广泛实验表明，我们的QARAD在图像级和像素级异常检测任务中均优于最先进方法，在准确性和鲁棒性方面有所提升，并且相比基于扩散的方法合成速度提高了5倍。 我们的完整代码和合成数据集将公开可用。