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+.

使用近身摄像视频序列预测人类意图的意图动作预测问题，介绍了一种利用整个视频序列特征和尾部视频序列特征的层次化完整 - 最近 (HCR) 信息融合模型。通过引导 - 反馈循环 (GFL) 机制和多尺度完整 - 最近特征聚合 (MCRFA) 模块，HCR 模型能够层次化地探索多尺度完整特征和多尺度最近特征之间的丰富相互关系。通过对 EPIC-Kitchens 和 EGTEA Gaze + 两个众所周知的公共数据集的比较和消融实验，验证了我们模型的有效性。