# Untrimmed Action Anticipation

arXiv: https://arxiv.org/abs/2202.04132 · HTML: https://mlsanity.com/paper/2202.04132/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attention](https://mlsanity.com/paper/1905.09035/) (2019)
- [Predicting the Future from First Person (Egocentric) Vision: A Survey](https://mlsanity.com/paper/2107.13411/) (2021)
- [Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks](https://mlsanity.com/paper/1604.05633/) (2016)
- [StartNet: Online Detection of Action Start in Untrimmed Videos](https://mlsanity.com/paper/1903.09868/) (2019)
- [The Evolution of First Person Vision Methods: A Survey](https://mlsanity.com/paper/1409.1484/) (2014)
- [Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video](https://mlsanity.com/paper/1911.10967/) (2019)
- [Personalized Dynamics Models for Adaptive Assistive Navigation Systems](https://mlsanity.com/paper/1804.04118/) (2018)
- [Temporal Aggregate Representations for Long-Range Video Understanding](https://mlsanity.com/paper/2006.00830/) (2020)
- [Towards Streaming Egocentric Action Anticipation](https://mlsanity.com/paper/2110.05386/) (2021)

## Built on by (most cited first, 7 of 7)

- [An Outlook into the Future of Egocentric Vision](https://mlsanity.com/paper/2308.07123/) (2023)
- [Can't make an Omelette without Breaking some Eggs: Plausible Action Anticipation using Large Video-Language Models](https://mlsanity.com/paper/2405.20305/) (2024)
- [Rethinking Learning Approaches for Long-Term Action Anticipation](https://mlsanity.com/paper/2210.11566/) (2022)
- [Streaming egocentric action anticipation: An evaluation scheme and approach](https://mlsanity.com/paper/2306.16682/) (2023)
- [Graphing the Future: Activity and Next Active Object Prediction using Graph-based Activity Representations](https://mlsanity.com/paper/2209.05194/) (2022)
- [Inductive Attention for Video Action Anticipation](https://mlsanity.com/paper/2212.08830/) (2022)
- [PhilHumans: Benchmarking Machine Learning for Personal Health](https://mlsanity.com/paper/2405.02770/) (2024)

## Abstract (arXiv)

Egocentric action anticipation consists in predicting a future action the camera wearer will perform from egocentric video. While the task has recently attracted the attention of the research community, current approaches assume that the input videos are "trimmed", meaning that a short video sequence is sampled a fixed time before the beginning of the action. We argue that, despite the recent advances in the field, trimmed action anticipation has a limited applicability in real-world scenarios where it is important to deal with "untrimmed" video inputs and it cannot be assumed that the exact moment in which the action will begin is known at test time. To overcome such limitations, we propose an untrimmed action anticipation task, which, similarly to temporal action detection, assumes that the input video is untrimmed at test time, while still requiring predictions to be made before the actions actually take place. We design an evaluation procedure for methods designed to address this novel task, and compare several baselines on the EPIC-KITCHENS-100 dataset. Experiments show that the performance of current models designed for trimmed action anticipation is very limited and more research on this task is required.