Skeleton MixFormer: Multivariate Topology Representation for Skeleton-based Action Recognition
Author:
Affiliation:
1. Xidian University, Xi'an, China
2. University of Macau, Macau, China
3. Xi'an University of Technology, Xi'an, China
Funder
The National Key R&D Program of China
The Teaching Reform Project of Shaanxi Higher Continuing Education
The National Natural Science Foundations of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3581783.3611900
Reference46 articles.
1. STAR-Transformer: A Spatio-temporal Cross Attention Transformer for Human Action Recognition
2. Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition
3. Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition
4. Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition
5. Skeleton-Based Action Recognition With Shift Graph Convolutional Network
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1. HDBN: A Novel Hybrid Dual-Branch Network for Robust Skeleton-Based Action Recognition;2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW);2024-07-15
2. A Hybrid Multi-Perspective Complementary Model for Human Skeleton-Based Action Recognition;2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW);2024-07-15
3. Temporal channel reconfiguration multi‐graph convolution network for skeleton‐based action recognition;IET Computer Vision;2024-04-17
4. Fusing angular features for skeleton‐based action recognition using multi‐stream graph convolution network;IET Image Processing;2024-03-27
5. Multi-View Time-Series Hypergraph Neural Network for Action Recognition;IEEE Transactions on Image Processing;2024
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