Large-Scale Self-Supervised Human Activity Recognition

被引：0

作者：

Zadeh, Mohammad Zaki ^{[1
]}

Jaiswal, Ashish ^{[1
]}

Pavel, Hamza Reza ^{[1
]}

Hebri, Aref ^{[1
]}

Kapoor, Rithik ^{[1
]}

Makedon, Fillia ^{[1
]}

机构：

[1] Univ Texas Arlington, Arlington, TX 76019 USA

来源：

PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON PERVASIVE TECHNOLOGIES RELATED TO ASSISTIVE ENVIRONMENTS, PETRA 2022 | 2022年

关键词：

computer vision; deep learning; self-supervised learning;

D O I：

10.1145/3529190.3534720

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, a self-supervised approach is used to obtain an effective human activity representation using a limited set of annotated data. This research is aimed on acquiring human activity representation in order to improve the accuracy of classifying videos of human activities in the NTU RGB+D 120 dataset. The effectiveness of various self-supervised approaches, as well as a supervised method, is studied. The results reveal that when the training set gets smaller, the performance of supervised learning approaches diminishes, whereas self-supervised methods maintain their performance by utilizing unlabeled data.

引用

页码：298 / 299

页数：2

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