# Variable Rate Independently Recurrent Neural Network (IndRNN) for Action Recognition
## 5 Experiments
### Results on UOW Large Scale Combined (LSC) Dataset  
The UOW LSC dataset [34] is a large dataset composed of nine publicly available datasets including MSR Action3D Ext [39], UTKinect [40], MSR DailyActivity [41], MSR Ac  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **CS** & **CV** \\ \hline Baseline IndRNN & 81.55\% & 87.12\% \\ Baseline IndRNN Test with 2\(\times\) rate & 63.5\% & 66.65\% \\ \hline Variable Rate IndRNN & 79.52\% & 88.00\% \\ Variable Rate IndRNN Test with 2\(\times\) rate & 79.04\% & 88.31\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of training and testing with different sampling rates.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **CS** & **CV** \\ \hline Deep learning on Lie Group [36] & 61.37\% & 66.95\% \\ JTM + CNN [17] & 73.40\% & 75.20\% \\ Res-TCN [19] & 74.30\% & 83.10\% \\ SkeletonNet (CNN) [18] & 75.94\% & 81.16\% \\ JDM + CNN [22] & 76.20\% & 82.30\% \\ Clips + CNN + MTLN [21] & 79.57\% & 84.83\% \\ Enhanced Visualization + CNN [37] & 80.03\% & 87.21\% \\
1 Layer RNN [6] & 56.02\% & 60.24\% \\
2 Layer RNN [6] & 56.29\% & 64.09\% \\
1 Layer LSTM [6] & 59.14\% & 66.81\% \\
2 Layer LSTM [6] & 60.09\% & 67.29\% \\
1 Layer PLSTM [6] & 62.05\% & 69.40\% \\
2 Layer PLSTM [6] & 62.93\% & 70.27\% \\ JL\_d + RNN [14] & 70.26\% & 82.39\% \\ STA-LSTM [30] & 73.40\% & 81.20\% \\ ST-LSTM + Trust Gate [7] & 69.20\% & 77.70\% \\ Pose conditioned STA-LSTM [38] & 77.10\% & 84.50\% \\ IndRNN [10] & 81.80\% & 87.97\% \\ Proposed & 84.32\% & 89.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of all skeleton-based methods on NTU RGB+D dataset.  
tionPair [15], CAD120 [42], CAD60 [43], G3D [44], RGBD-HuDa [45], and UTD-MHAD [46]. There are 94 actions in total. However, samples in some action classes do not contain skeleton modality and thus are excluded in the experiment, resulting in 88 action classes and 3897 samples. The protocols developed in [34] are used for evaluation, including a random cross subject (similar as in the NTU RGB+D setting) and random cross sample. For the random cross sample setting, in each action category, half of the samples are randomly selected for training. The average recall and precision of all classes, as suggested in [34], is used for comparison. The batch size is set to 64, and the dropping probability is set to 0.35 for both protocols. The other settings are the same as those used for the above NTU RGB+D dataset. Table 3 shows the result of the proposed model compared with the existing methods (results obtained from their original papers). It can be seen that better performance can be achieved with the proposed method against the RNN-based ones. For the cross sample setting, it also achieves similar performance as the graph-convolution-based AGNN [24], but worse in the cross subject setting. It is known that graph convolution can obtain better spatial features than simply providing joint coordinates to the RNNs. Therefore, better spatial feature extraction methods are desired for the proposed method and other RNN-based methods.