# GCN-Former: A Method for Action Recognition Using Graph Convolutional Networks and Transformer
## 4 Conclusions  
The main contribution of this study is the development of a spatiotemporal graph convolutional network framework, GCN-Former, which cleverly combines the spatial aggregation capabilities of graph convolutional networks (GCNs) and the temporal modeling advantages of the Transformer architecture. We introduce an improved contextual \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Settings** & **Param** & **FLOPs** & **Acc (\%)** \\ \hline GCN-Former (Ours) & 1.7 M & 1.88 G & 90.5 \\ \hline +SE [49] & 1.9 M & 1.89 G & 90.8 \\ \hline +CBAM [50] & 1.8 M & 1.89 G & 90.8 \\ \hline +CA [51] & 1.9 M & 1.95 G & 91.0 \\ \hline +CoT & 1.7 M & 1.90 G & 91.0 \\ \hline +CTA(Ours) & 1.8 M & 1.92 G & 91.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of attention mechanisms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Net**} & & & \\ \hline
**GCN** & **TCN** & **CTA** &
\begin{tabular}{c} **Transformer** \\ **Block** \\ \end{tabular} & **Param** & **FLOPs** & **Acc (\%)** \\ \hline \(\surd\) & \(\surd\) & & & 1.5 M & 1.97 G & 88.9 \\ \hline \(\surd\) & \(\surd\) & \(\surd\) & & 1.6 M & 1.99 G & 89.2 \\ \hline \(\surd\) & & & \(\surd\) & 1.7 M & 1.88 G & 90.5 \\ \hline \(\surd\) & & \(\surd\) & \(\surd\) & 1.8 M & 1.92 G & 91.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experiment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**GCN**} & \multicolumn{3}{c}{**Convolution Attention**} & \multirow{2}{*}{**Acc (\%)**} \\ \cline{2-3} \cline{4-5}  & **h = 1** & **h = 2** & **h = 4** & \\ \hline \(\surd\) & \(\surd\) & & & 90.4 \\ \(\surd\) & & \(\surd\) & & 90.9 \\ \(\surd\) & & & \(\surd\) & 91.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Number of convolutional attention heads. temporal attention (CTA) mechanism, which not only effectively captures the long-range dependencies in long-span action sequences but also retains key local detail features. The experimental results show that GCN-Former achieves state-of-the-art performance on multiple evaluation metrics on the widely used NTU RGB+D 60 and NTU RGB+D 120 datasets, which proves the effectiveness and superiority of the method. It is also proved on the UT-Kinect dataset that GCN-Former has obvious performance advantages in processing action recognition tasks involving long time sequences and can more effectively capture and understand long-range dependencies in complex action patterns. In addition, through ablation experimental analysis, we verify the positive impact of each component of the model on the overall performance improvement, further confirming the rationality of the model design. However, despite the significant progress made by GCN-Former, there are still some limitations. For example, the performance of the model may be limited for extremely complex action sequences or highly dynamically changing action patterns. Future work will focus on optimizing the CTA module to better handle complex scenes and explore more effective strategies to reduce computational costs and improve the efficiency of real-time applications. At the same time, incorporating more diverse information sources into the model, such as audio or environmental perception data, to enhance the model's ability to understand human movements in different situations and provide more comprehensive technical support and theoretical basis for skeleton-based action recognition should be considered. This not only broadens the scope of application of current research, but also highlights the direction for subsequent research. The current study focuses on directly comparing performance indicators to evaluate algorithm performance without in-depth exploration of statistically significant differences between algorithms. Although this approach provides an intuitive comparison, the introduction of more rigorous statistical test methods (such as non-parametric Friedman Aligned Ranking (FAR) and post hoc Finner test) [52] will enhance the reliability and robustness of the conclusions.