  Video action detectors are usually trained using datasets with
fully-supervised temporal annotations. Building such datasets is an expensive
task. To alleviate this problem, recent methods have tried to leverage weak
labeling, where videos are untrimmed and only a video-level label is available.
In this paper, we propose RefineLoc, a novel weakly-supervised temporal action
localization method. RefineLoc uses an iterative refinement approach by
estimating and training on snippet-level pseudo ground truth at every
iteration. We show the benefit of this iterative approach and present an
extensive analysis of five different pseudo ground truth generators. We show
the effectiveness of our model on two standard action datasets, ActivityNet
v1.2 and THUMOS14. RefineLoc shows competitive results with the
state-of-the-art in weakly-supervised temporal localization. Additionally, our
iterative refinement process is able to significantly improve the performance
of two state-of-the-art methods, setting a new state-of-the-art on THUMOS14.
