# Intention Action Anticipation Model with Guide-Feedback Loop Mechanism

arXiv: https://arxiv.org/abs/2403.12450 · HTML: https://mlsanity.com/paper/2403.12450/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 21 of 21)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [Learning to Detect Human-Object Interactions](https://mlsanity.com/paper/1702.05448/) (2017)
- [Anticipative Video Transformer](https://mlsanity.com/paper/2106.02036/) (2021)
- [Grounded Human-Object Interaction Hotspots from Video](https://mlsanity.com/paper/1812.04558/) (2018)
- [Peeking into the Future: Predicting Future Person Activities and Locations in Videos](https://mlsanity.com/paper/1902.03748/) (2019)
- [Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos](https://mlsanity.com/paper/2204.01696/) (2022)
- [What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attention](https://mlsanity.com/paper/1905.09035/) (2019)
- [Online Action Detection](https://mlsanity.com/paper/1604.06506/) (2016)
- [Forecasting Action through Contact Representations from First Person Video](https://mlsanity.com/paper/2102.00649/) (2021)
- [Learning to Anticipate Egocentric Actions by Imagination](https://mlsanity.com/paper/2101.04924/) (2021)
- [Pairwise Body-Part Attention for Recognizing Human-Object Interactions](https://mlsanity.com/paper/1807.10889/) (2018)
- [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)
- [Multi-Modal Temporal Convolutional Network for Anticipating Actions in Egocentric Videos](https://mlsanity.com/paper/2107.09504/) (2021)
- [ST-HOI: A Spatial-Temporal Baseline for Human-Object Interaction Detection in Videos](https://mlsanity.com/paper/2105.11731/) (2021)
- [SlowFast Rolling-Unrolling LSTMs for Action Anticipation in Egocentric Videos](https://mlsanity.com/paper/2109.00829/) (2021)
- [Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction](https://mlsanity.com/paper/1802.10503/) (2018)
- [TransAction: ICL-SJTU Submission to EPIC-Kitchens Action Anticipation Challenge 2021](https://mlsanity.com/paper/2107.13259/) (2021)
- [Streaming egocentric action anticipation: An evaluation scheme and approach](https://mlsanity.com/paper/2306.16682/) (2023)

## Built on by (most cited first, 1 of 1)

- [Egocentric Human-Object Interaction Detection: A New Benchmark and Method](https://mlsanity.com/paper/2506.14189/) (2025)

## Abstract (arXiv)

Anticipating human intention from videos has broad applications, such as automatic driving, robot assistive technology, and virtual reality. This study addresses the problem of intention action anticipation using egocentric video sequences to estimate actions that indicate human intention. We propose a Hierarchical Complete-Recent (HCR) information fusion model that makes full use of the features of the entire video sequence (i.e., complete features) and the features of the video tail sequence (i.e., recent features). The HCR model has two primary mechanisms. The Guide-Feedback Loop (GFL) mechanism is proposed to model the relation between one recent feature and one complete feature. Based on GFL, the MultiComplete-Recent Feature Aggregation (MCRFA) module is proposed to model the relation of one recent feature with multiscale complete features. Based on GFL and MCRFA, the HCR model can hierarchically explore the rich interrelationships between multiscale complete features and multiscale recent features. Through comparative and ablation experiments, we validate the effectiveness of our model on two well-known public datasets: EPIC-Kitchens and EGTEA Gaze+.