# Action Recognition Network Based on Local Spatiotemporal Features and Global Temporal Excitation
## 4 Experiments
### Comparisons with the State of the Art  
We compared LSCT networks with state-of-the-art action recognition approaches on Something-Something V1 and V2. Tables 1 and 2 display the combined data, which includes the method, backbone, frames, FLOPs (G), top-1 (%), and top-5 (%). Something-Something v1 dataset. According to Table 1, the baseline approach TSN [16] receives very low recognition results compared with the other methods, which shows the significance of temporal modeling in action recognition. In contrast to the TSM baseline approach [17], LSCT network achieves a higher accuracy with relatively low FLOPs. When sampling eight frames as input, our LSCT network achieves 3.7% accuracy improvement over TSM, with only a slight increase in FLOPs to 34G. Action recognition approaches of Table 1 can be classified into two types. The first type is 3 DCNN-based methods, including I3D [31], ECO [15], SAST [8], and GST [23]. The 3 DCNN-based methods have a large network model, which causes high FLOPs. In contrast, our proposed LSCT network achieves superior performance with low FLOPs, outperforming these 3 DCNN-based methods. Specifically, our LSCT network achieves a 5% accuracy improvement over SAST and achieves 2.3% accuracy improvement over GST when sampling eight frames as input. The second category is 2 DCNN-based methods, including TSM [17], STM [25], TRN [32], and TEI [19]. Our proposed LSCT network outperforms these methods. When sampling eight frames as input, despite a slight increase in FLOPs to 34G, our LSCT network achieves 1.9% accuracy improvement over TEI. Moreover, it also achieves competitive results compared with STM, which employs a three-crop strategy. Among the above methods, our LSCT network achieves the highest accuracy while maintaining relatively low FLOPs, demonstrating the effectiveness of our LSCT net \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & **Frames** & **FLOPs (G)** & **Pre-Trained** & **Top-1 (\%)** & **Top-5 (\%)** \\ \hline I3D [31] & Resnet-50 & 32 \(\times\) 2 & 153 \(\times\) 2 & ImageNet + K400 & 41.6 & 72.2 \\ ECO [15] & BNInception + ResNet-18 & 8 & 32 & Kinetics-400 & 39.6 & - \\ ECO [15] & BNInception + ResNet-18 & 92 & 267 & Kinetics-400 & 46.4 & - \\ SAST [8] & BNInception + ResNet-18 & 16 & - & Kinetics-400 & 44.3 & - \\ SAST [8] & BNInception + ResNet-18 & 32 & - & Kinetics-400 & 45.6 & - \\ TSN [16] & Resnet-50 & 8 & 33 & Kinetics-400 & 19.5 & 46.6 \\ TSN [16] & Resnet-50 & 16 & 66 & Kinetics-400 & 19.7 & 47.3 \\ TSM [17] & Resnet-50 & 8 & 33 & ImageNet & 45.6 & 74.2 \\ TSM [17] & Resnet-50 & 16 & 66 & ImageNet & 47.2 & 77.1 \\ TRN [32] & BNInception & 8 & 16 & ImageNet & 40.6 & - \\ GST [23] & Resnet-50 & 8 & 29.5 & ImageNet & 47.0 & 76.1 \\ GST [23] & Resnet-50 & 16 & 59 & ImageNet & 48.6 & 77.9 \\ STM [25] & Resnet-50 & \(8\times 30\) & \(33\times 30\) & ImageNet & 49.2 & 79.3 \\ STM [25] & Resnet-50 & \(16\times 30\) & \(67\times 30\) & ImageNet & 51.0 & 80.4 \\ TEI [19] & Resnet-50 & 8 & 33 & ImageNet & 47.4 & - \\ TEI [19] & Resnet-50 & 16 & 66 & ImageNet & 49.9 & - \\ LSCT (Ours) & Resnet-50 & 8 & 34 & ImageNet & 49.3 & 78.4 \\ LSCT (Ours) & Resnet-50 & 16 & 68 & ImageNet & 50.6 & 79.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results compared with state of the arts on Something-Something V1.