Consistency-Based Semi-Supervised Learning for Object Detection

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.

Syllabus

Intro
Outline of this presentation
Understanding the problem and terminologies: Object Detecto
Type of object detector quick review
Understanding the problem and terminologies: Motivation
A common approach in Semi-supervised learning
Consistency regularization
Loss function
Overall loss for Object Detector
Background Elimination
Experiments
Results: Consistency loss without unlabeled data
Limitations
Conclusion