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

共 50 条

[41] Self-supervised anomaly pattern detection for large scale industrial data
Tang, Xiaoyue
Zeng, Shan
Yu, Fang
Yu, Wei
Sheng, Zhongyin
Kang, Zhen
NEUROCOMPUTING, 2023, 515 : 1 - 12
[42] Self-Supervised Learning for Activity Recognition Based on Datasets With Imbalanced Classes
Hamad, Rebeen Ali
Woo, Wai Lok
Wei, Bo
Yang, Longzhi
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2025,
[43] SelfAct : Personalized Activity Recognition Based on Self-Supervised and Active Learning
Arrotta, Luca
Civitarese, Gabriele
Bettini, Claudio
MOBILE AND UBIQUITOUS SYSTEMS: COMPUTING, NETWORKING AND SERVICES, MOBIQUITOUS 2023, PT I, 2024, 593 : 375 - 391
[44] Hierarchical iterative and self-supervised method for concept-word acquisition from a large-scale Chinese corpora
Tian, GG
Cao, CG
PROCEEDINGS OF THE 2005 IEEE INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE PROCESSING AND KNOWLEDGE ENGINEERING (IEEE NLP-KE'05), 2005, : 312 - 317
[45] Self-Supervised Human Activity Representation for Embodied Cognition Assessment
Zaki Zadeh, Mohammad
Ramesh Babu, Ashwin
Jaiswal, Ashish
Makedon, Fillia
TECHNOLOGIES, 2022, 10 (01)
[46] Self-supervised Learning for Building Damage Assessment from Large-Scale xBD Satellite Imagery Benchmark Datasets
Xia, Zaishuo
Li, Zelin
Bai, Yanbing
Yu, Jinze
Adriano, Bruno
DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2022, PT I, 2022, 13426 : 373 - 386
[47] Using Large Self-Supervised Models for Low-Resource Speech Recognition
Krishna, D. N.
Wang, Pinyi
Bozza, Bruno
INTERSPEECH 2021, 2021, : 2436 - 2440
[48] A Washing Machine is All You Need? On the Feasibility of Machine Data for Self-Supervised Human Activity Recognition
Haresamudram, Harish
Essa, Irfan
Plotz, Thomas
2024 INTERNATIONAL CONFERENCE ON ACTIVITY AND BEHAVIOR COMPUTING, ABC 2024, 2024,
[49] CrossHAR: Generalizing Cross-dataset Human Activity Recognition via Hierarchical Self-Supervised Pretraining
Hong, Zhiqing
Li, Zelong
Zhong, Shuxin
Lyu, Wenjun
Wang, Haotian
Ding, Yi
He, Tian
Zhang, Desheng
PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT, 2024, 8 (02):
[50] Dynamic Temperature Scaling in Contrastive Self-Supervised Learning for Sensor-Based Human Activity Recognition
Khaertdinov, Bulat
Asteriadis, Stylianos
Ghaleb, Esam
IEEE TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIENCE, 2022, 4 (04): : 498 - 507

← 1 2 3 4 5 →