VisA

TopoTTA integrates persistent homology into test-time adaptation for anomaly segmentation

Researchers introduce TopoTTA, a framework that incorporates persistent homology from topological data analysis into test-time adaptation (TTA) for anomaly segmentation. The method applies multi-level cubical complex filtration to anomaly score maps to generate topological pseudo-labels that guide a lightweight test-time classifier, avoiding pixel-level heuristics like confidence thresholding. Evaluated across six benchmarks including MVTec AD, VisA, and MVTec 3D-AD, TopoTTA achieves an average 15% F1 improvement over state-of-the-art unsupervised methods, with the largest gains on geometrically complex defects.