🔍 Anomaly Detection

📷 CVPR2026 · 2 paper notes

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

ANoCo redefines anomaly detection from "how similar is this patch to normal ones" to "how much cost is required to pull this patch back to the normal manifold." By minimizing an anchored bipartite graph Laplacian energy to pull query patches toward the normal manifold, the displacement magnitude itself serves as the anomaly score. This approach requires no training, no message passing, and provides a closed-form solution, achieving new SOTA results on MVTec-AD / VisA in 1/2/4-shot settings.

LayoutAD: Exploring Semantic-Geometric Misalignment Reasoning for Scene Layout Anomaly Detection

LayoutAD proposes a new task "Scene Layout Anomaly Detection," which uses an unsupervised approach to generate object-level anomaly scores for each object in an image. By decomposing the scene into semantic and geometric graphs and reasoning the "misalignment" between them via cross-graph attention, it identifies layout-level hallucinations—such as "a five-legged dog" or "a car parked on a lake"—that are invisible to pixel-level detectors.