The ability to anticipate possible future human actions is essential for a wide range of applications, including autonomous driving and human-robot interaction. Consequently, numerous methods have been introduced for action anticipation in recent years, with deep learning-based approaches being particularly popular. In this work, we review the recent advances of action anticipation algorithms with a particular focus on daily-living scenarios. Additionally, we classify these methods according to their primary contributions and summarize them in tabular form, allowing readers to grasp the details at a glance. Furthermore, we delve into the common evaluation metrics and datasets used for action anticipation and provide future directions with systematical discussions.
@article{arxiv.2309.17257,
title = {A Survey on Deep Learning Techniques for Action Anticipation},
author = {Zeyun Zhong and Manuel Martin and Michael Voit and Juergen Gall and Jürgen Beyerer},
journal= {arXiv preprint arXiv:2309.17257},
year = {2026}
}
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