# RED: Reinforced Encoder-Decoder Networks for Action Anticipation

arXiv: https://arxiv.org/abs/1707.04818 · HTML: https://mlsanity.com/paper/1707.04818/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2017.

## Built on by (most cited first, 30 of 78)

- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Affordances from Human Videos as a Versatile Representation for Robotics](https://mlsanity.com/paper/2304.08488/) (2023)
- [Anticipative Video Transformer](https://mlsanity.com/paper/2106.02036/) (2021)
- [Long Short-Term Transformer for Online Action Detection](https://mlsanity.com/paper/2107.03377/) (2021)
- [OadTR: Online Action Detection with Transformers](https://mlsanity.com/paper/2106.11149/) (2021)
- [HierVL: Learning Hierarchical Video-Language Embeddings](https://mlsanity.com/paper/2301.02311/) (2023)
- [Real-time Online Video Detection with Temporal Smoothing Transformers](https://mlsanity.com/paper/2209.09236/) (2022)
- [HRP: Human Affordances for Robotic Pre-Training](https://mlsanity.com/paper/2407.18911/) (2024)
- [Anticipative Feature Fusion Transformer for Multi-Modal Action Anticipation](https://mlsanity.com/paper/2210.12649/) (2022)
- [Learning to Anticipate Egocentric Actions by Imagination](https://mlsanity.com/paper/2101.04924/) (2021)
- [Memory-and-Anticipation Transformer for Online Action Understanding](https://mlsanity.com/paper/2308.07893/) (2023)
- [Anticipating human actions by correlating past with the future with Jaccard similarity measures](https://mlsanity.com/paper/2105.12414/) (2021)
- [Predicting the Future from First Person (Egocentric) Vision: A Survey](https://mlsanity.com/paper/2107.13411/) (2021)
- [MECCANO: A Multimodal Egocentric Dataset for Humans Behavior Understanding in the Industrial-like Domain](https://mlsanity.com/paper/2209.08691/) (2022)
- [Video-Mined Task Graphs for Keystep Recognition in Instructional Videos](https://mlsanity.com/paper/2307.08763/) (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)
- [Deep Learning-based Action Detection in Untrimmed Videos: A Survey](https://mlsanity.com/paper/2110.00111/) (2021)
- [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)
- [GateHUB: Gated History Unit with Background Suppression for Online Action Detection](https://mlsanity.com/paper/2206.04668/) (2022)
- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [Multi-Modal Temporal Convolutional Network for Anticipating Actions in Egocentric Videos](https://mlsanity.com/paper/2107.09504/) (2021)
- [StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipation](https://mlsanity.com/paper/2304.03959/) (2023)
- [Colar: Effective and Efficient Online Action Detection by Consulting Exemplars](https://mlsanity.com/paper/2203.01057/) (2022)
- [SlowFast Rolling-Unrolling LSTMs for Action Anticipation in Egocentric Videos](https://mlsanity.com/paper/2109.00829/) (2021)
- [On the Pitfalls of Batch Normalization for End-to-End Video Learning: A Study on Surgical Workflow Analysis](https://mlsanity.com/paper/2203.07976/) (2022)
- [ExpertAF: Expert Actionable Feedback from Video](https://mlsanity.com/paper/2408.00672/) (2024)
- [Generative Adversarial Network for Future Hand Segmentation from Egocentric Video](https://mlsanity.com/paper/2203.11305/) (2022)
- [Coupling Intent and Action for Pedestrian Crossing Behavior Prediction](https://mlsanity.com/paper/2105.04133/) (2021)
- [Temporally smooth online action detection using cycle-consistent future anticipation](https://mlsanity.com/paper/2104.08030/) (2021)

## Abstract (arXiv)

Action anticipation aims to detect an action before it happens. Many real world applications in robotics and surveillance are related to this predictive capability. Current methods address this problem by first anticipating visual representations of future frames and then categorizing the anticipated representations to actions. However, anticipation is based on a single past frame's representation, which ignores the history trend. Besides, it can only anticipate a fixed future time. We propose a Reinforced Encoder-Decoder (RED) network for action anticipation. RED takes multiple history representations as input and learns to anticipate a sequence of future representations. One salient aspect of RED is that a reinforcement module is adopted to provide sequence-level supervision; the reward function is designed to encourage the system to make correct predictions as early as possible. We test RED on TVSeries, THUMOS-14 and TV-Human-Interaction datasets for action anticipation and achieve state-of-the-art performance on all datasets.