Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation
Reported by arXiv cs.AI: Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation. Analysis and context written by TickrWire.
Researchers propose TopoTTA, a topology-aware test-time adaptation method for anomaly segmentation that preserves structural consistency under noise and texture variation by leveraging higher-order spatial relationships.
Test-time adaptation (TTA) has emerged as a promising paradigm for mitigating distribution shifts in deep models. However, existing TTA approaches for anomaly segmentation remain limited by their reliance on pixel-level heuristics, such as confidence thresholding or entropy minimisation, which fail to preserve structural consistency under noise and texture variation. Moreover, they typically treat anomaly maps as flat intensity fields, ignoring the higher-order spatial relationships that characterise complex defect geometries. We introduce TopoTTA (Topological Test-Time Adaptation), a novel fr
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