5 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…
FLASH: A "Generate Once, Synthesize Many" Framework for Synthetic Anomaly Generation in Industrial Anomaly Detection
Abhay Kumar Das, Rajesh Gangireddy, Ashwin Vaidya +1
Synthetic anomaly generation helps expand industrial anomaly datasets when real defects are scarce or unavailable. Existing approaches lie at two extremes: procedural approaches ar…
Hierarchy-Aware Fine-Tuning of Vision-Language Models
Jiayu Li, Rajesh Gangireddy, Samet Akcay +2
Vision-Language Models (VLMs) learn powerful multimodal representations through large-scale image-text pretraining, but adapting them to hierarchical classification is underexplore…
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…