# Action Completion: A Temporal Model for Moment Detection

arXiv: https://arxiv.org/abs/1805.06749 · HTML: https://mlsanity.com/paper/1805.06749/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2018.

## Built on by (most cited first, 5 of 5)

- [Learning Object State Changes in Videos: An Open-World Perspective](https://mlsanity.com/paper/2312.11782/) (2023)
- [TVNet: Temporal Voting Network for Action Localization](https://mlsanity.com/paper/2201.00434/) (2022)
- [Video alignment using unsupervised learning of local and global features](https://mlsanity.com/paper/2304.06841/) (2023)
- [Is there progress in activity progress prediction?](https://mlsanity.com/paper/2308.05533/) (2023)
- [How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs](https://mlsanity.com/paper/2203.12344/) (2022)

## Abstract (arXiv)

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.