# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 4 Experiments
### Comparison with the State of the Art
#### 4.3.1 Something-Something V1  
Table 4 shows our results on the Something-Something V1 datasets. The table is divided into three parts, where the top section lists methods based on 3D CNN [9; 10; 37] the middle section lists methods based on 2D CNN [7; 11; 15; 16; 17; 30; 35; 38; 39] and the proposed SMEN-based method is presented in the bottom section. The \"Params\" in the table represents the number of learnable parameters in each model, mainly including the weights of the convolution kernels and the bias terms. The larger the number of parameters, the more memory the model requires.  
Compared to 3D CNN-based methods [9; 37], our model has fewer parameters and achieves higher accuracy in video action-recognition tasks. Compared to 2D CNN-based methods [7; 11; 15; 16; 17; 30; 39], our SMEN achieves top-1 accuracies of 52.4% and 54.4% when using 8 or 16 video frames as input, respectively, showing improvements of 6.8% (52.4% vs. 45.6%) and 7.2% (54.4% vs. 47.2%) over the TSM baseline. Furthermore, even in comparison  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Backbone** & **Pre-Trained** & **Frame \(\times\) Clip \(\times\) Scrops** & **Params** & **Top-1 (\%)** & **Top-5 (\%)** \\ \hline
**3 DCNN:** & & & & & & \\ ECO\({}_{\text{En}}\)Lite [37] & BNIncept+3D & Kinetics-400 & \(92\times 1\times 1\) & 150 M & 46.4 & - \\  & ResNet18 & & & & & \\ I3D from [9] & ResNet50 & ImageNet+ Kinetics-400 & \(32\times 2\times 1\) & 28.0 M & 41.6 & 72.2 \\  & NL-3D from [9] & ResNet50 & ImageNet+ Kinetics-400 & \(32\times 2\times 1\) & 35.3 M & 44.4 & 76.0 \\  & NL-3D + GCN [9] & ResNet50 & ImageNet+ Kinetics-400 & \(32\times 2\times 1\) & 62.2 M & 46.1 & 76.8 \\
3D DenseNet-121 [10] & 3D DenseNet-121 & ImageNet & \(16\times 1\times 1\) & 21.4 M & 50.2 & 78.9 \\ \hline
**2 DCNN:** & & & & & & \\ TSN [15] & BNInception & ImageNet & \(8\times 1\times 1\) & 10.7 M & 19.5 & - \\ TSN [7] & ResNet50 & ImageNet & \(8\times 1\times 1\) & 24.3 M & 45.6 & 74.2 \\  & ResNet50 & ImageNet & \(16\times 1\times 1\) & 24.3 M & 47.2 & 77.1 \\  & ResNet50 & ImageNet & \(16\times 1\times 1\) & 25.6 M & 47.6 & 77.7 \\  & TCM [11] & ResNet50 & ImageNet & \(8\times 1\times 1\) & 24.5 M & 52.2 & 80.4 \\  & MSNet [16] & ResNet50 & ImageNet & \(8\times 1\times 1\) & 24.6 M & 50.9 & 80.3 \\  & AGN [30] & ResNet50 & ImageNet & \(8\times 1\times 1\) & 27.6 M & 51.6 & 80.9 \\  & TEA [17] & ResNet50 & ImageNet & \(16\times 1\times 1\) & 24.5 M & 51.9 & 80.3 \\  & MDAF [35] & ResNet50 & ImageNet & \(8\times 1\times 1\) & 24.5 M & 49.1 & 78.0 \\  & MT-Net [38] & ResNet50 & ImageNet & \(16\times 1\times 1\) & - & 53.4 & 81.3 \\ \hline  & SMEN (ours) & ResNet50 & ImageNet & \(8\times 1\times 1\) & 26.1 M & 52.4 & 80.6 \\  & SMEN (ours) & ResNet50 & ImageNet & \(16\times 1\times 1\) & 26.1 M & 54.4 & 82.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison on Something-Something V1, where “-” denotes a result that is not given in the original references.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Iteration Times** & **Top-1 (\%)** \\ \hline
0 & 51.3 \\
1 & 51.8 \\
2 & 52.1 \\
3 & 52.4 \\
4 & 52.2 \\
5 & 51.6 \\
6 & 49.9 \\
7 & 49.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The impact of feedback iteration times for video action-recognition accuracy.  
with recent 2D CNN-based methods [35; 38], our model continues to show competitive performance improvement for video action-recognition tasks.  
Especially, on one hand, compared to feedforward attention-based methods [17; 39], our model effectively exploits contextual information from high-level features through the proposed FAGM attention mechanism. The contextual semantic information is leveraged to guide the low-level feature learning significant sub-action information, leading to higher recognition accuracy. On the other hand, compared to methods that capture sub-action information [11; 16; 30], our model can adaptively fuse sub-actions with different motion tempos, focusing more on key sub-action information.