A Survey on Deep Learning Techniques for Action Anticipation

2023/09/29 by Zeyun Zhong, Zhong, Zeyun, Manuel Martín +7 · 3 citations
Computer Science · Psychology · #Computer Vision and Pattern Recognition (cs.CV) #Digital Mental Health Interventions #FOS: Computer and information sciences #Human-Automation Interaction and Safety #Reinforcement Learning in Robotics

paper · pdf · doi:10.48550/arxiv.2309.17257

openalex publication_date 2023/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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• MixANT: Observation-dependent Memory Propagation for Stochastic Dense Action Anticipation

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