"Detecting the Unexpected: Practical Approaches to Anomaly Detection in Visual Data"

Anomaly detection is one of computer vision's most exciting and essential challenges today. From spotting subtle defects in manufacturing to identifying edge cases in model behavior, it is one of computer vision's most exciting and crucial challenges. In this session, we’ll do a hands-on walkthrough using the MVTec AD dataset, showcasing real-world workflows for data curation, exploration, and model evaluation. We’ll also explore the power of embedding visualizations and similarity searches to uncover hidden patterns and surface anomalies that often go unnoticed. This session is packed with actionable strategies to help you make sense of your data and build more robust, reliable models. Join us as we connect the dots between data, models, and real-world deployment—alongside other experts driving innovation in anomaly detection.

Paula Ramos holds a PhD in Computer Vision and over 20 years of experience creating AI, robotics, and agriculture technologies. She has focused her career on building intelligent, low-cost edge and Smart IoT systems that empower farmers and underserved communities.

Her work blends research and real-world impact—from yield estimation in remote coffee regions to full-stack AI solutions. As an AI Evangelist at Intel, she became known for bridging technical and non-technical audiences with clarity and inspiration.

Now a Senior Computer Vision & ML Advocate at Voxel51, Paula helps researchers and developers build better visual AI with open-source tools like FiftyOne. Her mission is to democratize AI and create meaningful change through technology.