Thesis Defense: Computing Features in Computer Vision for Event Detection

Thesis Defense: Computing Features in Computer Vision for Event Detection

Watch a 40-minute thesis defense presentation by Yang Yang at the University of Central Florida exploring advanced computing features in computer vision. Delve into hand-designed features, feature learning problems, and a proposed method for event detection using learned filters and data-driven concepts. Examine the hybrid learning model, comparing it to generative and discriminative approaches. Explore applications in human and horse detection, and gain insights into gated auto-encoders for modeling relationships between videos. Conclude with a discussion on K-shot learning, composite datasets, and computational cost comparisons in this comprehensive exploration of cutting-edge computer vision techniques.