2024/03/19 by Zongnan Ma, Ma, Zongnan, Fuchun Zhang +5 · 1 citation
Computer Science · Decision Sciences · #Advanced Decision-Making Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Simulation Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2403.12450
openalex publication_date 2024/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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+.