Anomaly detection—also known as novelty or out-of-distribution detection—is a key challenge in computer vision and pattern recognition. From medical imaging to industrial inspection, spotting what doesn’t belong is critical, yet notoriously hard. Why? Because anomalies can take unlimited forms, and most models see only a narrow slice of the possible "normal" during training.
The VAND workshop brings together cutting-edge research tackling this open-set problem across supervised, semi-supervised, and unsupervised methods, as well as few-, one-, and zero-shot approaches.
This year, we're also hosting two exciting challenges: (1) 'Adapt & Detect – Robust anomaly detection in real-world applications', and (2) 'VLM Anomaly Challenge – Few-shot learning for logical and structural anomaly detection using vision-language models'.
Join us to explore the next generation of models that can detect the unexpected.