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Time-Conditioned Action Anticipation in One Shot

conference contribution
posted on 2023-11-29, 18:10 authored by Qiuhong Ke, Mario FritzMario Fritz, Bernt Schiele
The goal of human action anticipation is to predict future actions. Ideally, in real-world applications such as video surveillance and self-driving systems, future actions should not only be predicted with high accuracy but also at arbitrary and variable time-horizons ranging from shortto long-term predictions. Current work mostly focuses on predicting the next action and thus long-term prediction is achieved by recursive prediction of each next action, which is both inefficient and accumulates errors. In this paper, we propose a novel time-conditioned method for efficient and effective long-term action anticipation. There are two key ingredients to our approach. First, by explicitly conditioning our anticipation network on time allows to efficiently anticipate also long-term actions. And second, we propose an attended temporal feature and a time-conditioned skip connection to extract relevant and useful information from observations for effective anticipation. We conduct extensive experiments on the large-scale Epic-Kitchen and the 50Salads Datasets. Experimental results show that the proposed method is capable of anticipating future actions at both short-term and long-term, and achieves state-of-theart performance.


Preferred Citation

Qiuhong Ke, Mario Fritz and Bernt Schiele. Time-Conditioned Action Anticipation in One Shot. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.

Primary Research Area

  • Trustworthy Information Processing

Secondary Research Area

  • Secure Connected and Mobile Systems

Name of Conference

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Legacy Posted Date


Open Access Type

  • Gold


@inproceedings{cispa_all_2814, title = "Time-Conditioned Action Anticipation in One Shot", author = "Ke, Qiuhong and Fritz, Mario and Schiele, Bernt", booktitle="{IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}", year="2019", }

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