# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 4 Experiments
### Visualization
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Backbone** & **Pre-Trained** & **Frame \(\times\) Clip \(\times\) Crops** & **Params** & **Top-1 (\%)** & **Top-5 (\%)** \\ \hline
**3 DCNN** & & & & & & \\ ECO [37] & BNIncep+3D-ResNet-18 & Scratch & \(92\times 1\times 1\) & 150 M & 70.0 & 89.4 \\ ISD [9] & Inception & ImageNet & \(64\times\) N/A \(\times\) N/A & 28.0 M & 72.1 & 90.3 \\ SlowFast [29] & ResNet50 & Scratch & \((4+32)\times 1\times 3\) & - & 75.6 & 92.1 \\ \hline
**2 DCNN**: & & & & & & \\ TSN [15] & BNInception & ImageNet & \(25\times 1\times 10\) & 10.7 M & 72.5 & 90.2 \\ TSM [7] & ResNet50 & ImageNet & \(8\times 10\times 3\) & 24.3 M & 74.1 & 91.2 \\ TGM [11] & ResNet50 & ImageNet & \(8\times 10\times 3\) & 24.5M & 76.1 & 92.3 \\ STM [12] & ResNet50 & ImageNet & \(16\times 10\times 3\) & 24.0 M & 73.7 & 91.6 \\ MSNet [16] & ResNet50 & ImageNet & \(8\times 10\times 3\) & 24.5 M & 75.0 & - \\ GSFNet [43] & ResNet50 & ImageNet & \(8\times 10\times 3\) & 24.0M & 74.8 & - \\ MDAF [35] & ResNet50 & ImageNet & \(8\times 10\times 3\) & 24.5 M & 76.2 & 92.0 \\ TEINet [40] & ResNet50 & ImageNet & \(8\times 10\times 3\) & 30.4 M & 74.9 & 91.9 \\ MVNTet [44] & ResNet50 & ImageNet & \(8\times 10\times 3\) & 24.3 M & 76.0 & 92.4 \\ GTDNet [45] & ResNet50 & ImageNet & \(8\times 10\times 3\) & 33.9 M & 75.5 & 92.5 \\ GTDNet [45] & ResNet50 & ImageNet & \(16\times 10\times 3\) & 33.9 M & 76.8 & 92.3 \\ \hline SMEN (ours) & ResNet50 & ImageNet & \(8\times 10\times 3\) & 26.1 M & 76.9 & 92.8 \\ SMEN (ours) & ResNet50 & ImageNet & \(16\times 10\times 3\) & 26.1 M & 77.9 & 93.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison with the state of the art on the Kinetics-400 dataset. The best results are highlighted. “N/A” and “-” denote a result that is not given in the original references. The visualization results of Figure 11 show that, for the first action category, compared to TSM, the proposed SMEN captures the motion tempos and the temporal changes of sub-action \"Picked up\" more promptly. Furthermore, for the subsequent sub-action \"Covering\" the cup with a towel, our model captures the entire \"Covering\" tempo more accurately. For the second action category, SMEN outperforms TSM by more effectively capturing the sub-actions \"Placing one object on another\" and \"Removing the arm\" while focusing more attention on these dynamic sub-actions. Our method avoids the allocation of resources to static information that has less impact on video action classification. Ablation studies and results confirm the effectiveness of modules in the proposed SMEN method. The performance comparisons demonstrate the effectiveness of the proposed SMEN, due to the model's capacity to capture and represent key sub-action information with different motion tempos more effectively. Additionally, the visualization analysis offers a more intuitive insight into the working mechanism of SMEN. Figure 10: On the Kinetics-400 dataset, we use Grad-CAM [46] to visualize the activation maps for the actions “Triple jump”, “Long jump” and “High jump”, with deep features extracted using TSM and the proposed SMEN method, respectively.