  Monitoring the progression of an action towards completion offers fine
grained insight into the actor's behaviour. In this work, we target detecting
the completion moment of actions, that is the moment when the action's goal has
been successfully accomplished. This has potential applications from
surveillance to assistive living and human-robot interactions. Previous effort
required human annotations of the completion moment for training (i.e. full
supervision). In this work, we present an approach for moment detection from
weak video-level labels. Given both complete and incomplete sequences, of the
same action, we learn temporal attention, along with accumulated completion
prediction from all frames in the sequence. We also demonstrate how the
approach can be used when completion moment supervision is available. We
evaluate and compare our approach on actions from three datasets, namely HMDB,
UCF101 and RGBD-AC, and show that temporal attention improves detection in both
weakly-supervised and fully-supervised settings.
