  Point-Level temporal action localization (PTAL) aims to localize actions in
untrimmed videos with only one timestamp annotation for each action instance.
Existing methods adopt the frame-level prediction paradigm to learn from the
sparse single-frame labels. However, such a framework inevitably suffers from a
large solution space. This paper attempts to explore the proposal-based
prediction paradigm for point-level annotations, which has the advantage of
more constrained solution space and consistent predictions among neighboring
frames. The point-level annotations are first used as the keypoint supervision
to train a keypoint detector. At the location prediction stage, a simple but
effective mapper module, which enables back-propagation of training errors, is
then introduced to bridge the fully-supervised framework with weak supervision.
To our best of knowledge, this is the first work to leverage the
fully-supervised paradigm for the point-level setting. Experiments on THUMOS14,
BEOID, and GTEA verify the effectiveness of our proposed method both
quantitatively and qualitatively, and demonstrate that our method outperforms
state-of-the-art methods.
