# Uncertainty-Aware Anticipation of Activities

arXiv: https://arxiv.org/abs/1908.09540 · HTML: https://mlsanity.com/paper/1908.09540/

## Key facts

- Cited by 38 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 30 of 38)

- [Future Transformer for Long-term Action Anticipation](https://mlsanity.com/paper/2205.14022/) (2022)
- [P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervision](https://mlsanity.com/paper/2205.02300/) (2022)
- [Learning to Anticipate Egocentric Actions by Imagination](https://mlsanity.com/paper/2101.04924/) (2021)
- [PDPP: Projected Diffusion for Procedure Planning in Instructional Videos](https://mlsanity.com/paper/2303.14676/) (2023)
- [Intention-Conditioned Long-Term Human Egocentric Action Forecasting](https://mlsanity.com/paper/2207.12080/) (2022)
- [Self-Regulated Learning for Egocentric Video Activity Anticipation](https://mlsanity.com/paper/2111.11631/) (2021)
- [Rethinking Learning Approaches for Long-Term Action Anticipation](https://mlsanity.com/paper/2210.11566/) (2022)
- [SCHEMA: State CHangEs MAtter for Procedure Planning in Instructional Videos](https://mlsanity.com/paper/2403.01599/) (2024)
- [Event-Guided Procedure Planning from Instructional Videos with Text Supervision](https://mlsanity.com/paper/2308.08885/) (2023)
- [Predicting the Next Action by Modeling the Abstract Goal](https://mlsanity.com/paper/2209.05044/) (2022)
- [Weakly-Supervised Online Action Segmentation in Multi-View Instructional Videos](https://mlsanity.com/paper/2203.13309/) (2022)
- [A Survey on Deep Learning Techniques for Action Anticipation](https://mlsanity.com/paper/2309.17257/) (2023)
- [Multimodal Large Models Are Effective Action Anticipators](https://mlsanity.com/paper/2501.00795/) (2025)
- [Semantically Guided Representation Learning For Action Anticipation](https://mlsanity.com/paper/2407.02309/) (2024)
- [Action Anticipation from SoccerNet Football Video Broadcasts](https://mlsanity.com/paper/2504.12021/) (2025)
- [Human Action Anticipation: A Survey](https://mlsanity.com/paper/2410.14045/) (2024)
- [Masked Diffusion with Task-awareness for Procedure Planning in Instructional Videos](https://mlsanity.com/paper/2309.07409/) (2023)
- [Uncertainty-boosted Robust Video Activity Anticipation](https://mlsanity.com/paper/2404.18648/) (2024)
- [Higher Order Recurrent Space-Time Transformer for Video Action Prediction](https://mlsanity.com/paper/2104.08665/) (2021)
- [A-ACT: Action Anticipation through Cycle Transformations](https://mlsanity.com/paper/2204.00942/) (2022)
- [RAP: Retrieval-Augmented Planner for Adaptive Procedure Planning in Instructional Videos](https://mlsanity.com/paper/2403.18600/) (2024)
- [Gated Temporal Diffusion for Stochastic Long-Term Dense Anticipation](https://mlsanity.com/paper/2407.11954/) (2024)
- [Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction](https://mlsanity.com/paper/2107.05140/) (2021)
- [FutureHuman3D: Forecasting Complex Long-Term 3D Human Behavior from Video Observations](https://mlsanity.com/paper/2211.14309/) (2022)
- [Modeling Events and Interactions through Temporal Processes -- A Survey](https://mlsanity.com/paper/2303.06067/) (2023)
- [Masked Temporal Interpolation Diffusion for Procedure Planning in Instructional Videos](https://mlsanity.com/paper/2507.03393/) (2025)
- [VS-TransGRU: A Novel Transformer-GRU-based Framework Enhanced by Visual-Semantic Fusion for Egocentric Action Anticipation](https://mlsanity.com/paper/2307.03918/) (2023)
- [Knowledge-Guided Short-Context Action Anticipation in Human-Centric Videos](https://mlsanity.com/paper/2309.05943/) (2023)
- [ActFusion: a Unified Diffusion Model for Action Segmentation and Anticipation](https://mlsanity.com/paper/2412.04353/) (2024)
- [MANTA: Diffusion Mamba for Efficient and Effective Stochastic Long-Term Dense Anticipation](https://mlsanity.com/paper/2501.08837/) (2025)

## Abstract (arXiv)

Anticipating future activities in video is a task with many practical applications. While earlier approaches are limited to just a few seconds in the future, the prediction time horizon has just recently been extended to several minutes in the future. However, as increasing the predicted time horizon, the future becomes more uncertain and models that generate a single prediction fail at capturing the different possible future activities. In this paper, we address the uncertainty modelling for predicting long-term future activities. Both an action model and a length model are trained to model the probability distribution of the future activities. At test time, we sample from the predicted distributions multiple samples that correspond to the different possible sequences of future activities. Our model is evaluated on two challenging datasets and shows a good performance in capturing the multi-modal future activities without compromising the accuracy when predicting a single sequence of future activities.