Variation-aware directed graph convolutional networks for skeleton-based action recognition
Authors:
- Tianchen LiTLTianchen Li
- Pei GengPGPei Geng
- Guohui CaiGCGuohui Cai
- Xinran HouXHXinran Hou
- Xuequan LuXLXuequan Lu
- Lei LyuLLLei Lyu
- 2 August 2024
- journal article
- research article
- Published by Elsevier in Knowledge-Based Systems
- Vol. 302
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (61976127)
- National Natural Science Foundation of China (62276156)
- Natural Science Foundation of Shandong Province (ZR2021LZL012)
- Natural Science Foundation of Shandong Province (ZR2022LZH004)
- Natural Science Foundation of Shandong Province (ZR2021MF048)
- Taishan Scholar Foundation of Shandong Province (tsqn202306150)
This publication has 38 references indexed in Scilit:
- Exploring incomplete decoupling modeling with window and cross-window mechanism for skeleton-based action recognitionKnowledge-Based Systems, 2023
- Global spatio-temporal synergistic topology learning for skeleton-based action recognitionPattern Recognition, 2023
- Relation-mining self-attention network for skeleton-based human action recognitionPattern Recognition, 2023
- Spatio-temporal hard attention learning for skeleton-based activity recognitionPattern Recognition, 2023
- SpatioTemporal focus for skeleton-based action recognitionPattern Recognition, 2022
- Frequency-driven channel attention-augmented full-scale temporal modeling network for skeleton-based action recognitionKnowledge-Based Systems, 2022
- Towards a multimodal human activity dataset for healthcareMultimedia Systems, 2022
- Graph transformer network with temporal kernel attention for skeleton-based action recognitionKnowledge-Based Systems, 2022
- Tripool: Graph triplet pooling for 3D skeleton-based action recognitionPattern Recognition, 2021
- Driver action recognition using deformable and dilated faster R-CNN with optimized region proposalsApplied Intelligence, 2019