  Temporal action proposals are a common module in action detection pipelines
today. Most current methods for training action proposal modules rely on fully
supervised approaches that require large amounts of annotated temporal action
intervals in long video sequences. The large cost and effort in annotation that
this entails motivate us to study the problem of training proposal modules with
less supervision. In this work, we propose a semi-supervised learning algorithm
specifically designed for training temporal action proposal networks. When only
a small number of labels are available, our semi-supervised method generates
significantly better proposals than the fully-supervised counterpart and other
strong semi-supervised baselines. We validate our method on two challenging
action detection video datasets, ActivityNet v1.3 and THUMOS14. We show that
our semi-supervised approach consistently matches or outperforms the fully
supervised state-of-the-art approaches.

Computer Vision and Pattern Recognition