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SAFe: Segment-guided Aggregation of Feature Densities for Anomaly-aware Segmentation

arXiv · AI, language, vision and robotics · article · Sep 21, 2026 · UTC

Visual segmentation systems encounter objects outside their training distribution during real-world deployment, hindering reliable autonomous systems that depend on scene parsing in the perception stage. Many recent methods address this by using self-supervised foundation models to train density estimators that yield low likelihood in anomalous image regions. Although promising, these methods suffer from poor feature semantics or they lack spatial consistency, both of which undermine critical downstream decisions. We address this problem with~\method, a generative method based on class-conditi

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