Unsupervised anomaly detection (UAD) from images strives to model normal data distributions, creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the efficient and unified one-for-all scheme, challenges persist in accurately segmenting anomalies for further monitoring. Moreover, this problem is obscured by the widely-used AUROC metric under imbalanced UAD settings. This motivates us to emphasize the significance of precise segmentation of anomaly pixels using pAP and DSC as metrics. To address the unsolved segmentation task, we introduce the Unified Anomaly Segmentation (UniAS). UniAS presents a multi-level hybrid pipeline that progressively enhances normal information from coarse to fine, incorporating a novel multi-granularity gated CNN (MGG-CNN) into Transformer layers to explicitly aggregate local details from different granularities. UniAS achieves state-of-the-art anomaly segmentation performance, attaining 65.12/59.33 and 40.06/32.50 in pAP/DSC on the MVTec-AD and VisA datasets, respectively, surpassing previous methods significantly. The codes are shared at https://github.com/Mwxinnn/UniAS.

翻译：图像无监督异常检测旨在对正常数据分布进行建模，以创建具有判别性的表征来区分并精确定位异常。尽管近期高效统一的通用方案取得了进展，但在精确分割异常以进行进一步监测方面仍存在挑战。此外，在不平衡的无监督异常检测设置下，广泛使用的AUROC指标使此问题变得模糊。这促使我们强调使用pAP和DSC作为指标来精确分割异常像素的重要性。为解决这一尚未解决的异常分割任务，我们提出了统一异常分割方法。UniAS提出了一种多级混合流程，从粗到细逐步增强正常信息，将新颖的多粒度门控CNN集成到Transformer层中，以显式聚合来自不同粒度的局部细节。UniAS在异常分割性能上达到了最先进水平，在MVTec-AD和VisA数据集上分别取得了65.12/59.33和40.06/32.50的pAP/DSC分数，显著超越了先前的方法。代码已在https://github.com/Mwxinnn/UniAS共享。