Kavli Affiliate: Xiang Zhang
| First 5 Authors: Wenxin Ma, Qingsong Yao, Xiang Zhang, Zhelong Huang, Zihang Jiang
| Summary:
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.
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