Explore consistency-based semi-supervised learning for object detection in this 27-minute lecture from the University of Central Florida. Delve into the problem of object detection, review detector types, and understand the motivation behind semi-supervised learning approaches. Learn about consistency regularization, loss functions, and background elimination techniques. Examine experiments, results, and limitations of consistency loss without unlabeled data. Gain insights into this advanced computer vision topic through a comprehensive presentation, complete with slides for visual reference.