Identifying Human Daily Activity Types with Time-Aware Interactions
Human activities embedded in crowdsourced data, such as social media trajectory, represent individual daily styles and patterns, which are valuable in many applications. However, the accurate identification of human activity types (HATs) from social media is challenging, possibly because interaction...
Main Authors: | Renyao Chen, Hong Yao, Runjia Li, Xiaojun Kang, Shengwen Li, Lijun Dong, Junfang Gong |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-12-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/10/24/8922 |
Similar Items
-
Region-aware neural graph collaborative filtering for personalized recommendation
by: Shengwen Li, et al.
Published: (2022-12-01) -
Improving Skeleton-Based Action Recognition Using Part-Aware Graphs in a Multi-Stream Fusion Context
by: Zois Tsakiris, et al.
Published: (2023-01-01) -
Skeleton-Based Action Recognition With Low-Level Features of Adaptive Graph Convolutional Networks
by: Jialin Gang, et al.
Published: (2021-01-01) -
Gaze-Aware Graph Convolutional Network for Social Relation Recognition
by: Xingming Yang, et al.
Published: (2021-01-01) -
Skeleton Based Action Recognition Algorithm on Multi-modal Lightweight Graph Convolutional Network
by: SU Jiangyi, SONG Xiaoning, WU Xiaojun, YU Dongjun
Published: (2021-04-01)