# Am I Done? Predicting Action Progress in Videos

arXiv: https://arxiv.org/abs/1705.01781 · HTML: https://mlsanity.com/paper/1705.01781/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2017.

## Built on by (most cited first, 11 of 11)

- [Learning to Anticipate Egocentric Actions by Imagination](https://mlsanity.com/paper/2101.04924/) (2021)
- [Rank2Reward: Learning Shaped Reward Functions from Passive Video](https://mlsanity.com/paper/2404.14735/) (2024)
- [OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition](https://mlsanity.com/paper/2312.00096/) (2023)
- [TAMFormer: Multi-Modal Transformer with Learned Attention Mask for Early Intent Prediction](https://mlsanity.com/paper/2210.14714/) (2022)
- [Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition](https://mlsanity.com/paper/2202.11374/) (2022)
- [A-ACT: Action Anticipation through Cycle Transformations](https://mlsanity.com/paper/2204.00942/) (2022)
- [Coherent Temporal Synthesis for Incremental Action Segmentation](https://mlsanity.com/paper/2403.06102/) (2024)
- [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)
- [Multiview Progress Prediction of Robot Activities](https://mlsanity.com/paper/2603.00151/) (2026)
- [Shared Representation for 3D Pose Estimation, Action Classification, and Progress Prediction from Tactile Signals](https://mlsanity.com/paper/2603.25906/) (2026)

## Abstract (arXiv)

In this paper we deal with the problem of predicting action progress in videos. We argue that this is an extremely important task since it can be valuable for a wide range of interaction applications. To this end we introduce a novel approach, named ProgressNet, capable of predicting when an action takes place in a video, where it is located within the frames, and how far it has progressed during its execution. To provide a general definition of action progress, we ground our work in the linguistics literature, borrowing terms and concepts to understand which actions can be the subject of progress estimation. As a result, we define a categorization of actions and their phases. Motivated by the recent success obtained from the interaction of Convolutional and Recurrent Neural Networks, our model is based on a combination of the Faster R-CNN framework, to make frame-wise predictions, and LSTM networks, to estimate action progress through time. After introducing two evaluation protocols for the task at hand, we demonstrate the capability of our model to effectively predict action progress on the UCF-101 and J-HMDB datasets.