# Multi-Scale Receptive Fields Convolutional Network for Action Recognition
## 4 Experiments
### Comparison with State-of-the-Art Methods  
We then compare our method with other leading methods on Kinetics-400. Table 6 presents the corresponding results. P3D ResNet-50 is the baseline. MSRFNet reaches a 73.2% top-1 accuracy and a 88.6% top-5 accuracy, which is 7.7% and 2.3% higher than the baseline, respectively. We first note that MSRFNet has better performance than two-stream, which is based on 2D ConvNets. Secondly, MSRFNet outperforms 3D ConvNets and 3D ResNet-50 with fewer parameters. Thirdly, compared with some good-design models, such as I3D, R(2+1)D, and Slow-Only, MSRFNet is also a little better, which shows that the method of multi-scale receptive fields can improve the effect of the model. However, the top-5 accuracy is still slightly lower than those three models, which shows that the generalization ability of MSRFNet needs to be further strengthened.  
After training on Kinetics-400, MSRFNet is transferred to UCF101 and HMDB51 for training and testing using the transfer learning technology. The fully connected layer needs to be replaced with a new layer to match the number of action classes of new datasets. The new fully connected layer is initialized with random values and the parameters are  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Top-1** & **Top-5** \\ \hline ConvNet+LSTM [13] & 57.0\% & 79.0\% \\ Two-Stream [13] & 61.0\% & 81.3\% \\
3D ConvNets [13] & 56.1\% & 79.5\% \\
3D ResNet-50 [15] & 61.3\% & 83.1\% \\ I3D-RGB [8] & 72.1\% & 90.3\% \\ R(2+1)D-RGB [17] & 72.0\% & 90.0\% \\ Slow-Only [9] & 72.6\% & 90.3\% \\ P3D ResNet-50 & 65.5\% & 86.3\% \\
**MSFNet** & **73.2\%** & **88.6\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with state-of-the-art on Kinetics-400.  
Figure 9: Accuracy varies with the number of scales on HMDB51.  
updated during the transfer learning process. The experiments are conducted on three splits of UCF101 and HMDB51.  
Table 7 shows the transfer learning results on three splits of UCF101 and HMDB51. The results of MSRFNet on three splits are close to each other, which indicates that the model has stable performance on these two datasets. MSRFNet achieves the highest top-1 accuracy of 91.5% on split-2 of UCF101. The average top-1 accuracy is 91.0% and the average top-5 accuracy is 98.0%. On HMDB51, MSRFNet achieves the highest top-1 accuracy of 60.3% on split-1. Its average top-1 and top-5 accuracy are 59.9% and 87.2%, respectively.  
Table 8 lists the comparison results with other methods after transfer learning on UCF101 and HMDB51. We take the average accuracy of three splits as the comparison result. MSRFNet achieves a recognition accuracy of 91.0% on UCF101, which is 3.2% higher than P3D ResNet-50 (the baseline). Besides, the performance of MSRFNet exceeds most of the methods in the table. The recognition accuracy of MSRFNet is only lower than I3D. The main reason is that I3D has higher model capacity, so it will have good performance after pretraining on large-scale datasets. On HMDB51, the performance advantage is not as significant as that of UCF101. However, MSRFNet still outperforms the baseline by 5.8% on HMDB51. From the experimental results of UCF101 and HMDB51, the performance of MSRFNet is better than most action recognition methods, and the accuracy is significantly improved compared with the baseline, which substantially proves the effectiveness of the method of multi-scale receptive fields.