  In this paper, we introduce the concept of learning latent super-events from
activity videos, and present how it benefits activity detection in continuous
videos. We define a super-event as a set of multiple events occurring together
in videos with a particular temporal organization; it is the opposite concept
of sub-events. Real-world videos contain multiple activities and are rarely
segmented (e.g., surveillance videos), and learning latent super-events allows
the model to capture how the events are temporally related in videos. We design
temporal structure filters that enable the model to focus on particular
sub-intervals of the videos, and use them together with a soft attention
mechanism to learn representations of latent super-events. Super-event
representations are combined with per-frame or per-segment CNNs to provide
frame-level annotations. Our approach is designed to be fully differentiable,
enabling end-to-end learning of latent super-event representations jointly with
the activity detector using them. Our experiments with multiple public video
datasets confirm that the proposed concept of latent super-event learning
significantly benefits activity detection, advancing the state-of-the-arts.
