MSST-RT: Multi-Stream Spatial-Temporal Relative Transformer for Skeleton-Based Action Recognition.Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN).  Finally, four ST-RTs, which extract spatial-temporal features from four kinds of skeleton sequence, are fused to form the final model, multi-stream spatial-temporal relative transformer (MSST-RT), to enhance performance.  The results demonstrate that MSST-RT is on par with SOTA in terms of performance.
MSST-RT
MSST-RT: Multi-Stream Spatial-Temporal Relative Transformer for Skeleton-Based Action Recognition.Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN).  Finally, four ST-RTs, which extract spatial-temporal features from four kinds of skeleton sequence, are fused to form the final model, multi-stream spatial-temporal relative transformer (MSST-RT), to enhance performance.  The results demonstrate that MSST-RT is on par with SOTA in terms of performance.
MSST-RT
MSST-RT: Multi-Stream Spatial-Temporal Relative Transformer for Skeleton-Based Action Recognition.Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN).  The most important work is ST-GCN, which automatically learns both spatial and temporal patterns from skeleton sequences. 
GCN
The most important work is ST-GCN, which automatically learns both spatial and temporal patterns from skeleton sequences. 
ST-GCN
MSST-RT: Multi-Stream Spatial-Temporal Relative Transformer for Skeleton-Based Action Recognition.Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN).  In this work, we propose the use of a spatial-temporal relative transformer (ST-RT) to overcome these defects.  Through introducing relay nodes, ST-RT avoids the transformer architecture, breaking the inherent skeleton topology in spatial and the order of skeleton sequence in temporal dimensions.  Finally, four ST-RTs, which extract spatial-temporal features from four kinds of skeleton sequence, are fused to form the final model, multi-stream spatial-temporal relative transformer (MSST-RT), to enhance performance.  The results demonstrate that MSST-RT is on par with SOTA in terms of performance.
ST-RT
MSST-RT: Multi-Stream Spatial-Temporal Relative Transformer for Skeleton-Based Action Recognition.Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN).  Finally, four ST-RTs, which extract spatial-temporal features from four kinds of skeleton sequence, are fused to form the final model, multi-stream spatial-temporal relative transformer (MSST-RT), to enhance performance.  The results demonstrate that MSST-RT is on par with SOTA in terms of performance.
MSST-RT