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

Ali Zia, Usman Ali, Abdul Rehman, Umer Ramzan, Kang Han et al.|June 26, 2026arXiv

Key Takeaway

Using topological features (shape and connectivity patterns) during test-time adaptation significantly improves anomaly segmentation by preserving structural coherence that pixel-level methods miss, achieving 15% F1 improvement on standard benchmarks.

Summary

This paper introduces TopoTTA, a test-time adaptation framework for anomaly segmentation that uses topological data analysis (persistent homology) to preserve structural consistency in defect detection.

evaluation efficiency reasoning

Key Terms

test-time-adaptation persistent-homology anomaly-segmentation topological-data-analysis cubical-complex