Detect image anomalies from many classes using one model with UniAD
Detect image anomalies from many classes using one model with UniAD
A Unified Model for Multi-class Anomaly Detection
arXiv paper abstract https://arxiv.org/abs/2206.03687v1
arXiv PDF paper https://arxiv.org/pdf/2206.03687v1.pdf
Despite the rapid advance of unsupervised anomaly detection, existing methods require to train separate models for different objects.
... present UniAD that accomplishes anomaly detection for multiple classes with a unified framework.
... reconstruction networks may fall into an "identical shortcut", where both normal and anomalous samples can be well recovered, and hence fail to spot outliers.
To tackle ... First ... come up with a layer-wise query decoder to help model the multi-class distribution. Second, ... employ a neighbor masked attention module ...
Third, ... propose a feature jittering strategy that urges the model to recover the correct message even with noisy inputs.
... algorithm ... surpass the state-of-the-art alternatives by a sufficiently large margin ...
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