Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization 事件

Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization arXiv:2605.28428v1 Announce Type: new Abstract: Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anomaly detection is typically solved by measuring feature similarity of a query patch to a memory of normal patches. However, similarity alone does not reveal how strongly a query patch violates the structure of the normal fe