Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation

Detecting defects in manufactured goods — a crack in a circuit board, a tear in fabric — requires models that can generalize across wildly different visual conditions. TopoTTA brings an unusual tool to this problem: persistent homology, a mathematical framework that captures the shape and connectivity of structures across scales. Rather than relying on simple pixel-confidence thresholds, it uses topological features derived from anomaly score maps to generate more reliable pseudo-labels at test time. Evaluated across six benchmarks and both 2D and 3D data, it achieves a 15% average F1 improvement. Applications include industrial quality control, medical imaging anomaly detection, and autonomous inspection systems. Authors: Ali Zia, Usman Ali, Abdul Rehman, Umer Ramzan, Kang Han, Muhammad Faheem, Shahnawaz Qureshi, Wei Xiang Paper: https://arxiv.org/abs/2606.28268v1