# Auxiliary Task Graph Convolution Network: A Skeleton-Based Action Recognition for Practical Use
## 4 Experiments and Results
### Ablation Study  
To show the stepwise contribution of AT-GCN and static sample training methods, the study uses benchmarks X-sub and X-set of NTU RGB+D 120 dataset [18] and InfoGCN, HD-GCN [32; 33]. * Setting a static sample range: Using the 210 range as a baseline, decrease the static range by 1 s increments to find the static range that performs best with the original model. As shown in Table 2, a wider freeze range than necessary leads to poor performance due to too many dummy poses. The 120 range has the highest performance based on the original model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**NTU-RGB+D 60 (\%)**} & \multicolumn{2}{c}{**NTU-RGB+D 120 (\%)**} & \multirow{2}{*}{**NW-UCLA (\%)**} \\ \cline{2-2} \cline{4-6}  & **X-Sub** & **X-View** & **X-Sub** & **X-Set** \\ \hline ST-GCN [4] & 87.4 & 92.9 & 82.2 & 83.0 & 90.5 \\ ST-GCN w/AT & **88.0** & 93.3 & 82.6 & **83.5** & 91.2 \\ ST-GCN w/AT w/D & **88.0** & **93.9** & **82.7** & 83.2 & **91.4** \\ \hline MS-G3D [36] & 89.4 & 94.5 & 83.9 & 85.4 & 91.6 \\ MS-G3D w/AT & **89.6** & 94.7 & **84.2** & 85.6 & **92.9** \\ MS-G3D w/AT w/D & 89.5 & **94.9** & **84.2** & **85.7** & 92.7 \\ \hline CTR-GCN [5] & 89.3 & 94.5 & 83.9 & 84.7 & 91.6 \\ CTR-GCN w/AT & 89.5 & **95.0** & 84.0 & **85.3** & 92.7 \\ CTR-GCN w/AT w/D & **89.6** & **95.0** & **84.2** & **85.3** & **92.9** \\ \hline InfoGCN [32] & 90.0 & 94.4 & 86.0 & 87.1 & 92.9 \\ InfoGCN w/AT & **90.6** & **94.9** & 86.2 & **88.0** & **93.8** \\ InfoGCN w/AT w/D & 90.3 & 94.7 & **86.5** & 87.6 & 93.5 \\ \hline HD-GCN [33] & 89.0 & 94.7 & 83.8 & 84.9 & 91.4 \\ HD-GCN w/AT & **89.2** & **95.2** & 84.1 & 85.2 & 92.0 \\ HD-GCN w/AT w/D & **89.2** & **95.2** & **84.3** & **85.5** & **92.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons top-1 accuracy against NTU RGB+D 60, NTU RGB+D 120, NW-UCLA datasets. AT indicates the auxiliary task module. D indicates different sample ranges by pathway method. Bold is indicates best performance of each GCN model. * Ensemble for Auxiliary Task: To see how well the T ensemble fits the Auxiliary Task, the baseline is set for the performance of the AT-GCN model. The proposed ensemble method [32; 33] is used for performance comparison. As shown in Table 4, the existing ensemble method has less performance gain than the T ensemble because it is not designed to consider the Auxiliary Task. T ensemble shows the same or higher performance than the traditional ensemble method in AT-GCN. Table 5 shows that the performance gain of the T ensemble is similar for other networks except for MS-G3D [36]. * Effect of static range training methods: The traditional training and validation method is used as a baseline. The performance comparison results are shown in Table 6. Results show us traditional training performance drops when static validation is performed. Also, static training performance is similar to traditional scores when \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**InfoGCN (\%)**} & \multicolumn{2}{c}{**HD-GCN (\%)**} \\ \cline{2-5}  & **X-Sub** & **X-Set** & **X-Sub** & **X-Set** \\ \hline AT-GCN & 86.5 & 87.6 & 84.3 & 85.5 \\ Original, 3-en & 87.4 & 88.7 & 85.3 & 86.7 \\ T, 2-en & 87.4 & 88.5 & 85.1 & 86.4 \\ T, 3-en & **87.6** & **89.0** & **85.5** & **86.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of AT-GCN by ensemble. Original is the ensemble method of the original model. T 2-en is an ensemble of models trained with [120; 180], [150; 210] sample ranges. 3-en is the addition of models trained on the [180; 240] range. Bold is indicates best performance.