# Anticipation and next action forecasting in video: an end-to-end model with memory

arXiv: https://arxiv.org/abs/1901.03728 · HTML: https://mlsanity.com/paper/1901.03728/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos](https://mlsanity.com/paper/2104.07905/) (2021)
- [EgoEnv: Human-centric environment representations from egocentric video](https://mlsanity.com/paper/2207.11365/) (2022)
- [Cross-view Action Recognition Understanding From Exocentric to Egocentric Perspective](https://mlsanity.com/paper/2305.15699/) (2023)
- [Weakly-Supervised Dense Action Anticipation](https://mlsanity.com/paper/2111.07593/) (2021)

## Abstract (arXiv)

Action anticipation and forecasting in videos do not require a hat-trick, as far as there are signs in the context to foresee how actions are going to be deployed. Capturing these signs is hard because the context includes the past. We propose an end-to-end network for action anticipation and forecasting with memory, to both anticipate the current action and foresee the next one. Experiments on action sequence datasets show excellent results indicating that training on histories with a dynamic memory can significantly improve forecasting performance.