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Visual anomaly detection aims to learn normality from normal images, butexisting approaches are fragmented across various tasks: defect detection,semantic anomaly detection, multi-class anomaly detection, and anomalyclustering. This one-task-one-model approach is resource-intensive and incurshigh maintenance costs as the number of tasks increases. We present UniFormaly,a universal and powerful anomaly detection framework. We emphasize thenecessity of our off-the-shelf approach by pointing out a suboptimal issue inonline encoder-based methods. We introduce Back Patch Masking (BPM) and topk-ratio feature matching to achieve unified anomaly detection. BPM eliminatesirrelevant background regions using a self-attention map from self-supervisedViTs. This operates in a task-agnostic manner and alleviates memory storageconsumption, scaling to tasks with large-scale datasets. Top k-ratio featurematching unifies anomaly levels and tasks by casting anomaly scoring intomultiple instance learning. Finally, UniFormaly achieves outstanding results onvarious tasks and datasets. Codes are available athttps://github.com/YoojLee/Uniformaly.

UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection