22 citations · 23 across the 5 of their papers we have counts for
4 papers · 1 filter
Detecting Defects that Matter: An Application-Driven Benchmark for Anomaly Detection in Manufacturing and Retail Logistics (VAND 4.0 Challenge)
Lars Heckler-Kram, Dorian Henning, Ashwin Vaidya +6
Existing Anomaly Detection benchmarks are saturated and often unrealistic. As part of the VAND 4.0 Challenge, we introduce a hidden-test, application-driven benchmark across two de…
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,…
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…
Augmenting Zero-Shot Detection Training with Image Labels
Katharina Kornmeier, Ulla Scheler, Pascal Herrmann
Zero-shot detection (ZSD), i.e., detection on classes not seen during training, is essential for real world detection use-cases, but remains a difficult task. Recent research attem…