  We introduce completion moment detection for actions - the problem of
locating the moment of completion, when the action's goal is confidently
considered achieved. The paper proposes a joint classification-regression
recurrent model that predicts completion from a given frame, and then
integrates frame-level contributions to detect sequence-level completion
moment. We introduce a recurrent voting node that predicts the frame's relative
position of the completion moment by either classification or regression. The
method is also capable of detecting incompletion. For example, the method is
capable of detecting a missed ball-catch, as well as the moment at which the
ball is safely caught. We test the method on 16 actions from three public
datasets, covering sports as well as daily actions. Results show that when
combining contributions from frames prior to the completion moment as well as
frames post completion, the completion moment is detected within one second in
89% of all tested sequences.
