1 citations · 2 across the 4 of their papers we have counts for
4 papers · 1 filter
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,…
Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detection
Aimira Baitieva, Yacine Bouaouni, Alexandre Briot +3
Anomaly detection (AD) is essential for automating visual inspection in manufacturing. This field of computer vision is rapidly evolving, with increasing attention towards real-wor…
AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance
Joao P. C. Bertoldo, Dick Ameln, Ashwin Vaidya +1
Recent advances in visual anomaly detection research have seen AUROC and AUPRO scores on public benchmark datasets such as MVTec and VisA converge towards perfect recall, giving th…
Anomalib: A Deep Learning Library for Anomaly Detection
Samet Akcay, Dick Ameln, Ashwin Vaidya +3
This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides…