Document Type
Article
Abstract
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.
Publication Date
7-11-2022
Language
English
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Zadeh, Mohammad Zaki; Jaiswal, Ashish; Pavel, Hamza Reza; Hebri, Aref; Kapoor, Rithik; and Makedon, Fillia, "Large-Scale Self-Supervised Human Activity Recognition" (2022). Association of Computing Machinery Open Access Agreement Publications. 62.
https://mavmatrix.uta.edu/utalibraries_acmoapubs/62