# Predicting the Next Action by Modeling the Abstract Goal

arXiv: https://arxiv.org/abs/2209.05044 · HTML: https://mlsanity.com/paper/2209.05044/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 51 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Builds on (most cited first, 29 of 29)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [Rescaling Egocentric Vision](https://mlsanity.com/paper/2006.13256/) (2020)
- [A Recurrent Latent Variable Model for Sequential Data](https://mlsanity.com/paper/1506.02216/) (2015)
- [The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines](https://mlsanity.com/paper/2005.00343/) (2020)
- [Anticipative Video Transformer](https://mlsanity.com/paper/2106.02036/) (2021)
- [MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition](https://mlsanity.com/paper/2201.08383/) (2022)
- [Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video](https://mlsanity.com/paper/2005.02190/) (2020)
- [Sequential Neural Models with Stochastic Layers](https://mlsanity.com/paper/1605.07571/) (2016)
- [When will you do what? - Anticipating Temporal Occurrences of Activities](https://mlsanity.com/paper/1804.00892/) (2018)
- [What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attention](https://mlsanity.com/paper/1905.09035/) (2019)
- [Procedure Planning in Instructional Videos](https://mlsanity.com/paper/1907.01172/) (2019)
- [In the Eye of the Beholder: Gaze and Actions in First Person Video](https://mlsanity.com/paper/2006.00626/) (2020)
- [Future Transformer for Long-term Action Anticipation](https://mlsanity.com/paper/2205.14022/) (2022)
- [Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture](https://mlsanity.com/paper/1509.05016/) (2015)
- [Learning to Anticipate Egocentric Actions by Imagination](https://mlsanity.com/paper/2101.04924/) (2021)
- [Uncertainty-Aware Anticipation of Activities](https://mlsanity.com/paper/1908.09540/) (2019)
- [Encouraging LSTMs to Anticipate Actions Very Early](https://mlsanity.com/paper/1703.07023/) (2017)
- [Anticipating human actions by correlating past with the future with Jaccard similarity measures](https://mlsanity.com/paper/2105.12414/) (2021)
- [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)
- [A Variational Auto-Encoder Model for Stochastic Point Processes](https://mlsanity.com/paper/1904.03273/) (2019)
- [Multi-Modal Temporal Convolutional Network for Anticipating Actions in Egocentric Videos](https://mlsanity.com/paper/2107.09504/) (2021)
- [Action Anticipation with RBF Kernelized Feature Mapping RNN](https://mlsanity.com/paper/1911.07806/) (2019)
- [Forecasting future action sequences with attention: a new approach to weakly supervised action forecasting](https://mlsanity.com/paper/1912.04608/) (2019)
- [Forecasting Future Action Sequences with Neural Memory Networks](https://mlsanity.com/paper/1909.09278/) (2019)
- [TransAction: ICL-SJTU Submission to EPIC-Kitchens Action Anticipation Challenge 2021](https://mlsanity.com/paper/2107.13259/) (2021)

## Built on by (most cited first, 14 of 14)

- [AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?](https://mlsanity.com/paper/2307.16368/) (2023)
- [Interaction Region Visual Transformer for Egocentric Action Anticipation](https://mlsanity.com/paper/2211.14154/) (2022)
- [Can't make an Omelette without Breaking some Eggs: Plausible Action Anticipation using Large Video-Language Models](https://mlsanity.com/paper/2405.20305/) (2024)
- [WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition](https://mlsanity.com/paper/2304.05088/) (2023)
- [A Survey on Deep Learning Techniques for Action Anticipation](https://mlsanity.com/paper/2309.17257/) (2023)
- [Human Action Anticipation: A Survey](https://mlsanity.com/paper/2410.14045/) (2024)
- [On the Efficacy of Text-Based Input Modalities for Action Anticipation](https://mlsanity.com/paper/2401.12972/) (2024)
- [Neuro Symbolic Knowledge Reasoning for Procedural Video Question Answering](https://mlsanity.com/paper/2503.14957/) (2025)
- [MULTISCRIPT: Multimodal Script Learning for Supporting Open Domain Everyday Tasks](https://mlsanity.com/paper/2310.04965/) (2023)
- [SuPRA: Surgical Phase Recognition and Anticipation for Intra-Operative Planning](https://mlsanity.com/paper/2403.06200/) (2024)
- [From Recognition to Prediction: Leveraging Sequence Reasoning for Action Anticipation](https://mlsanity.com/paper/2408.02769/) (2024)
- [FactCheck: Feasibility-aware Long-term Action Anticipation with Multi-agent Collaboration](https://mlsanity.com/paper/2606.14778/) (2026)
- [Interact with me: Joint Egocentric Forecasting of Intent to Interact, Attitude and Social Actions](https://mlsanity.com/paper/2412.16698/) (2024)
- [SAGE: Synchronized Action-Gaze Recognition and Anticipation for Human Behavior Understanding](https://mlsanity.com/paper/2607.04017/) (2026)

## Abstract (arXiv)

The problem of anticipating human actions is an inherently uncertain one. However, we can reduce this uncertainty if we have a sense of the goal that the actor is trying to achieve. Here, we present an action anticipation model that leverages goal information for the purpose of reducing the uncertainty in future predictions. Since we do not possess goal information or the observed actions during inference, we resort to visual representation to encapsulate information about both actions and goals. Through this, we derive a novel concept called abstract goal which is conditioned on observed sequences of visual features for action anticipation. We design the abstract goal as a distribution whose parameters are estimated using a variational recurrent network. We sample multiple candidates for the next action and introduce a goal consistency measure to determine the best candidate that follows from the abstract goal. Our method obtains impressive results on the very challenging Epic-Kitchens55 (EK55), EK100, and EGTEA Gaze+ datasets. We obtain absolute improvements of +13.69, +11.24, and +5.19 for Top-1 verb, Top-1 noun, and Top-1 action anticipation accuracy respectively over prior state-of-the-art methods for seen kitchens (S1) of EK55. Similarly, we also obtain significant improvements in the unseen kitchens (S2) set for Top-1 verb (+10.75), noun (+5.84) and action (+2.87) anticipation. Similar trend is observed for EGTEA Gaze+ dataset, where absolute improvement of +9.9, +13.1 and +6.8 is obtained for noun, verb, and action anticipation. It is through the submission of this paper that our method is currently the new state-of-the-art for action anticipation in EK55 and EGTEA Gaze+ https://competitions.codalab.org/competitions/20071#results Code available at https://github.com/debadityaroy/Abstract_Goal