# Multi-Scale Receptive Fields Convolutional Network for Action Recognition
## 4 Experiments
### The Selection of Multiple Scales  
MSRFNet uses three different dilated convolutions, with the dilation rate being {1,2,3}. In this section, we provide an extensive experiment to evaluate MSRFNet with different numbers of scales. Table 5 shows the detailed settings. The number of scales is set to 1 to 6.The set {1,2,3,4,5,6} means six dilated convolution layers are adopted, with dilation rate being {1,2,3,4,5,6}. Other scale settings have the same meanings. The experiment is performed on the first split of UCF101 and HMDB51. Top-1 and top-5 performance are both reported in Table 5.  
It can be seen from the results on UCF101 that the top-1 and top-5 accuracy increases when the number of scales is added, which indicates that our method can help the model learn better representations. However, when the number of scales exceeds 3, the accuracy begins to decline. When the number of scales reaches 5, the top-1 accuracy of the model decreases to 56.9%, which is similar to the effect of the single scale. The main reason for the decrease of accuracy is that the enlargement of the receptive field will also amplify the influence of noise information, thus affecting the learning process of action representations. When the number of scales reaches 8, the negative effect of the large receptive field is further increased, and the top-1 accuracy drops to 56.8%. Figure 8 shows how accuracy on UCF101 varies with the number of scales. When the number of scales is no more than 3, the top-1 \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**UCF101**} & \multicolumn{4}{c}{**HMDB51**} \\ \cline{2-10}  & **Split-1** & **Split-2** & **Split-3** & **Average** & **Split-1** & **Split-2** & **Split-3** & **Average** \\ \hline P3D & 56.5\% & 55.7\% & 56.8\% & 56.3\% & 18.4\% & 17.3\% & 17.4\% & 17.7\% \\ ResNet-50 & **60.1\%** & **59.7\%** & **58.8\%** & **59.5\%** & **24.1\%** & **21.5\%** & **23.3\%** & **22.9\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy on three splits of UCF101 and HMDB51. P3D ResNet-50 is the baseline. accuracy and top-5 accuracy have increased, and the improvement is obvious. When the number of scales exceeds 3, the accuracy of the model decreases and never exceeds the situation when the number of scales is 3. This reflects that the positive effect reaches its maximum when the scale number is 3. We also show the accuracy variation on HMDB51 in Figure 9. The top-5 accuracy is similar to that of UCF101, but the top-1 accuracy shows different results. When the scale number is 5 or 6, the top-1 accuracy is slightly higher than when the scale number is 3. Although the top-1 accuracy further increases with the increase in the number of scales on HMDB51, the top-5 accuracy still decreases compared with when the number of scales is 3. In addition, when the number of scales further increases, the top-1 accuracy also decreases, which infers that excessive enlargement of the receptive field will indeed amplify the influence of noise information. It can also be seen from Table 5 that with the increase in the number of scales, the training time of a single epoch is correspondingly prolonged. Therefore, considering the experimental results on UCF101 and HMDB51, we finally adopt 3 dilated scales, which means the dilation rate is set as {1,2,3}. Figure 8: Accuracy varies with the number of scales on UCF101.