22 citations · 23 across the 5 of their papers we have counts for
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cs.CV2025
From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge
Lars Heckler-Kram, Ashwin Vaidya, Jan-Hendrik Neudeck +4
Visual anomaly detection is a strongly application-driven field of research. Consequently, the connection between academia and industry is of paramount importance. In this regard,…
cs.CV2025★ 22 cited
The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection
Lars Heckler-Kram, Jan-Hendrik Neudeck, Ulla Scheler +2
In recent years, performance on existing anomaly detection benchmarks like MVTec AD and VisA has started to saturate in terms of segmentation AU-PRO, with state-of-the-art models o…