22 citations · 23 across the 5 of their papers we have counts for
6 papers
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…
Self-Paced Absolute Learning Progress as a Regularized Approach to Curriculum Learning
Tobias Niehues, Ulla Scheler, Pascal Klink
The usability of Reinforcement Learning is restricted by the large computation times it requires. Curriculum Reinforcement Learning speeds up learning by defining a helpful order i…
Understanding Counterexamples for Relational Properties with DIbugger
Mihai Herda, Michael Kirsten, Etienne Brunner +6
Software verification is a tedious process that involves the analysis of multiple failed verification attempts, and adjustments of the program or specification. This is especially…