  Detecting actions in videos is an important yet challenging task. Previous
works usually utilize (a) sliding window paradigms, or (b) per-frame action
scoring and grouping to enumerate the possible temporal locations. Their
performances are also limited to the designs of sliding windows or grouping
strategies. In this paper, we present a simple and effective method for
temporal action proposal generation, named Deep Point-wise Prediction (DPP).
DPP simultaneously predicts the action existing possibility and the
corresponding temporal locations, without the utilization of any handcrafted
sliding window or grouping. The whole system is end-to-end trained with joint
loss of temporal action proposal classification and location prediction. We
conduct extensive experiments to verify its effectiveness, generality and
robustness on standard THUMOS14 dataset. DPP runs more than 1000 frames per
second, which largely satisfies the real-time requirement. The code is
available at https://github.com/liluxuan1997/DPP.
