# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 4 Experiments
### Comparison with the State of the Art
#### 4.3.3 Kinetics-400  
Kinetics-400 is a larger dataset with diverse appearances and backgrounds, compared to Something-Something V1 and V2. To assess the proposed method's effectiveness in modeling appearance features and interactions, we take performance comparisons on the Kinetics-400 dataset, as shown in Table 6.  
Compared to the 3D CNN-based method SlowFast [29], our SMEN model also obtains remarkable results in the Top-5 accuracy (93.3% vs. 92.1%) and its Top-1 accuracy is higher (76.9% vs. 75.6%) since our model can acquire more granular motion features. Compared to TSM, our method achieves a Top-1 accuracy of 76.9%, an improvement of 2.8%. For other 2D CNN-based methods, SMEN provides further performance gains with only a slight increase in model parameters, demonstrating its efficiency and effectiveness in appearance-based action datasets. Furthermore, our SMEN model obtains competitive performances on the Kinetics-400 datasets. Specifically, its Top-1 and Top-5 accuracy outperforms the state-of-the-art 2D CNN-based method MDAF [35] by 0.7% (76.9% vs. 76.2%) and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Backbone** & **Pre-Trained** & **Frame \(\times\) Clips \(\times\) Crops** & **Params** & **Top-1 (\%)** & **Top-5 (\%)** \\ \hline \multicolumn{7}{c}{**3 DCNN:**} \\
3D DenseNet-121 [10] & 3D DenseNet121 & ImageNet & \(16\times 1\times 1\) & 21.4 M & 62.9 & 88.0 \\ CIDC [41] & ResNet50 & ImageNet & \(32\times 1\times 1\) & 87 M & 56.3 & 83.7 \\ RubilisNet [42] & ResNet50 & ImageNet & \(8\times 1\times 1\) & - & 58.8 & 85.6 \\ \hline \multicolumn{7}{c}{**2 DCNN:**} \\ TSN [15] & BNInception & ImageNet & \(8\times 1\times 1\) & 10.7 M & 33.4 & - \\ TSM [7] & ResNet50 & ImageNet & \(8\times 1\times 1\) & 24.3 M & 58.8 & 85.4 \\ TSM [7] & ResNet50 & ImageNet & \(16\times 1\times 1\) & 24.3 M & 63.4 & 88.5 \\ STM [12] & ResNet50 & ImageNet & \(16\times 1\times 1\) & 35.3 M & 64.2 & 89.8 \\ MSNet [16] & ResNet50 & ImageNet & \(8\times 1\times 1\) & 24.6 M & 63.0 & 88.4 \\ AGPN [30] & ResNet50 & ImageNet & \(8\times 1\times 1\) & 27.6 M & 63.1 & 88.6 \\ ACTION-Net [18] & ResNet50 & ImageNet & \(16\times 1\times 1\) & 28.1 M & 64.0 & 89.3 \\ GSFNet [43] & ResNet50 & ImageNet & \(8\times 1\times 1\) & 24.0 M & 62.1 & - \\ MDAF [35] & ResNet50 & ImageNet & \(8\times 1\times 1\) & 24.5 M & 62.3 & 87.8 \\ TENet [40] & ResNet50 & ImageNet & \(16\times 1\times 1\) & 30.4 M & 61.3 & - \\ \hline SMEN (ours) & ResNet50 & ImageNet & \(8\times 1\times 1\) & 26.1 M & 63.3 & 88.8 \\ SMEN (ours) & ResNet50 & ImageNet & \(16\times 1\times 1\) & 26.1 M & 65.0 & 89.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison on Something-Something V2, where “-” denotes a result that is not given in the original references.  
0.8% (92.8% vs. 92.0%), respectively. All these results highlight that our SMEN is more effective and accurate than the feedforward network in modeling the sub-action motion tempos and representation learning for video action-recognition classification.