mlsanityData to 7 Oct 2026

Intention Action Anticipation Model with Guide-Feedback Loop Mechanism

arXiv 2403.12450 ↗First posted 19 Mar 2024cs.CVcited by 1

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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+.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 19 Mar 2024.

Builds on

21 of 37 references.

References · 21

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  2. 2016Temporal Segment Networks: Towards Good Practices for Deep Action Recognitioncited by 899
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  5. 2021Anticipative Video Transformercited by 197
  6. 2018Grounded Human-Object Interaction Hotspots from Videocited by 128
  7. 2019Peeking into the Future: Predicting Future Person Activities and Locations in Videoscited by 112
  8. 2022Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videoscited by 110
  9. 2019What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attentioncited by 104
  10. 2016Online Action Detectioncited by 68
  11. 2021Forecasting Action through Contact Representations from First Person Videocited by 46
  12. 2021Learning to Anticipate Egocentric Actions by Imaginationcited by 40
9 more
  1. 2018Pairwise Body-Part Attention for Recognizing Human-Object Interactionscited by 34
  2. 2021Self-Regulated Learning for Egocentric Video Activity Anticipationcited by 32
  3. 2022Rethinking Learning Approaches for Long-Term Action Anticipationcited by 29
  4. 2021Multi-Modal Temporal Convolutional Network for Anticipating Actions in Egocentric Videoscited by 23
  5. 2021ST-HOI: A Spatial-Temporal Baseline for Human-Object Interaction Detection in Videoscited by 20
  6. 2021SlowFast Rolling-Unrolling LSTMs for Action Anticipation in Egocentric Videoscited by 20
  7. 2018Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Predictioncited by 12
  8. 2021TransAction: ICL-SJTU Submission to EPIC-Kitchens Action Anticipation Challenge 2021cited by 6
  9. 2023Streaming egocentric action anticipation: An evaluation scheme and approachcited by 5

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  1. 2025Egocentric Human-Object Interaction Detection: A New Benchmark and Methodcited by 3