  Temporal action localization is an important step towards video
understanding. Most current action localization methods depend on untrimmed
videos with full temporal annotations of action instances. However, it is
expensive and time-consuming to annotate both action labels and temporal
boundaries of videos. To this end, we propose a weakly supervised temporal
action localization method that only requires video-level action instances as
supervision during training. We propose a classification module to generate
action labels for each segment in the video, and a deep metric learning module
to learn the similarity between different action instances. We jointly optimize
a balanced binary cross-entropy loss and a metric loss using a standard
backpropagation algorithm. Extensive experiments demonstrate the effectiveness
of both of these components in temporal localization. We evaluate our algorithm
on two challenging untrimmed video datasets: THUMOS14 and ActivityNet1.2. Our
approach improves the current state-of-the-art result for THUMOS14 by 6.5% mAP
at IoU threshold 0.5, and achieves competitive performance for ActivityNet1.2.
