# Dilated Multi-Temporal Modeling for Action Recognition
## 4 Results
### Ablation Studies
#### 4.4.2 Different Components  
The DMT module incorporates two components to model temporal information: an attention mechanism and the multiple-dilation (MD) operation. The attention mechanism  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Kernels 1** & **Proportion 2** & **Frames** & **Top-1** & **Top-5** & **Mean** \\ \hline
2 & 1/2 & 8 & 85.2 & 98.7 & 80.0 \\
2 & 1/4 & 8 & 86.9 & 98.8 & 82.1 \\
2 & 1/8 & 8 & 87.5 & 98.9 & 83.0 \\
2 & 1/16 & 8 & 86.7 & 98.8 & 82.2 \\ \hline
1 & 1/8 & 8 & 86.9 & 98.7 & 82.0 \\
2 & 1/8 & 8 & 87.5 & 98.9 & 83.0 \\
3 & 1/8 & 8 & 86.5 & 98.6 & 81.9 \\
4 & 1/8 & 8 & 86.4 & 98.8 & 81.7 \\ \hline \hline \end{tabular}  
\end{table}
Table 4: Ablation study on hyperparameters.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **Backbones** & **Training Input** & **GFLOPs 1** & **Top-1** & **Top-5** \\ \hline TSN [23] & InceptionV3 & \(3\times 224\times 224\) & \(3\times 250\) & 72.5 & 90.2 \\ ARTNet [39] & ResNet18 & \(16\times 112\times 112\) & \(24\times 250\) & 70.7 & 89.3 \\ I3D [15] & InceptionV3 & \(64\times 224\times 224\) & \(108\times\mathrm{N/A}\) & 72.1 & 90.3 \\ R(2+1)D [5] & ResNet34 & \(32\times 112\times 112\) & \(142\times 10\) & 74.3 & 91.4 \\ \hline NL 3D [29] & ResNet50 & \(128\times 224\times 224\) & \(282\times 30\) & 76.5 & 92.6 \\ ip-CSN [40] & ResNet50 & \(8\times 224\times 224\) & \(1.2\times 10\) & 70.8 & - \\ TSM [25] & ResNet50 & \(16\times 224\times 224\) & \(65\times 30\) & 74.7 & 91.4 \\ TEINet [38] & ResNet50 & \(16\times 224\times 224\) & \(86\times 30\) & 76.2 & 92.5 \\ bLVNet-TAM [36] & bResNet50 & \(48\times 224\times 224\) & \(93\times 9\) & 73.5 & 91.2 \\ SlowOnly [7] & ResNet50 & \(8\times 224\times 224\) & \(42\times 30\) & 74.8 & 91.6 \\ SlowFast\({}_{4\times 16}\)[7] & ResNet50 & (43+23) \(\geq 224\times 224\) & \(36\times 30\) & 75.6 & 92.1 \\ SlowFast\({}_{8\times 8}\)[7] & ResNet50 & (84+32) \(\geq 224\times 224\) & \(66\times 30\) & 77.0 & 92.6 \\ I3D * [15] & ResNet50 & \(32\times 224\times 224\) & \(335\times 30\) & 76.6 & - \\ TPN [10] & ResNet50 & \(8\times 224\times 224\) & - & 75.5 & 92.1 \\ TANet-50 [27] & ResNet50 & \(8\times 224\times 224\) & \(43\times 30\) & 76.3 & 92.6 \\ TANet-50 [27] & ResNet50 & \(16\times 224\times 224\) & \(86\times 12\) & 76.9 & 92.9 \\ STM [41] & ResNet50 & \(8\times 224\times 224\) & \(33\times 30\) & 75.5 & 92.0 \\ STM [41] & ResNet50 & \(16\times 224\times 224\) & \(67\times 30\) & 76.9 & 92.7 \\ \hline DMTNet & ResNet50 & \(8\times 224\times 224\) & \(43\times 30\) & 75.9 & 92.6 \\ DMTNet & ResNet50 & \(16\times 224\times 224\) & \(86\times 30\) & 77.1 & 93.0 \\ \hline \hline \end{tabular}  
\end{table}
Table 3: Kinetics400 results with other methods. In the evaluation, we instantiated our DMT with a ResNet50 backbone. To ensure a fair comparison, we employed an evaluation strategy using 8 and 16 frames, and we compared our method to others that use similar-scale backbone networks and the same evaluation metrics.  
learns weights that focus on the importance of both temporal and channel dimensions of the feature map, while the MD operation models multiscale temporal information. The effectiveness of the MD operation was evaluated and reported in Table 5. The results demonstrate that the model that utilizes both an attention mechanism and MD operation performed the best. Furthermore, the results indicate that the contribution of the MD operation to the improvement in accuracy is significant. This suggests that while the attention mechanism only weights the feature map, MD operation engages in both temporal interactions and multi-timescale modeling.