# Reconstructed Prototype Network Combined with CDC-TAGCN for Few-Shot Action Recognition
## 4 Experiments
### Result  
This paper will use DASTM (ECCV 2022) (DASTM composed of PN and AGCN; compared to our approach, PN and TCN are improved versions) as the baseline. One thing worth noting is that while there are some researchers in the field of skeleton action recognition, the dataset standards and experimental metrics could not be quite consistent. DASTM, as a recent research advancement, has elevated the challenge by using 100 classes for training, with 20 classes designated for validation and testing. Regarding parameter settings, the original AGCN encoder had nine layers. However, in the case of small samples, it is believed that there is no need for so many parameters, as having too many parameters can lead to overfitting. Therefore, this study sets the number of layers for AGCN to six. Table 1 displays information about the layers in seven rows, but most encoders in the skeleton recognition field do not count the initial layer as part of the total layers. This study  
Figure 5: This is the framework diagram of the encoder, which consists of a total of 6 layers. The \(1\times 1\) Conv represents the residual connections used between layers to improve model fitting. The features output by the encoder are then input into RC-PN. uses a structure with two layers of 64 channels, two layers of 128 channels, and two layers of 256 channels. This structure can initially capture some low-level features, and then, as the layers deepen and the number of channels increases, the model starts capturing more complex features. Tables 2 and 3 display the results for the 5-way-1-shot task and 5-way-5-shot task. The results in the tables are presented as mean + variance after ten tests run. From the experimental results in Table 1, it can be observed that even with only one sample, this paper's method has achieved good performance, demonstrating the feasibility of this approach. To ensure fairness, the same seed and parameters were used under the same dataset for consistency. In the 5-way-1-shot task, where only one sample is available; sample coefficient reconstruction is not possible, so only \(L_{RC}\) is active in RC-PN. The 5-way-5-shot task provides a complete demonstration of this paper's method's performance. Tables 2 and 3 showcase the improvements brought by RC-PN and CDC-TACN, with both experiments using DASTM as the baseline. Both NTU-S and NTU-T are based on NTU RGB + D 120. The difference is that the number of samples collected in each class in NTU-T is twice that of NTU-S. With fixed parameters, NTU-S and NTU-T have at most 30 and 60 class prototypes per class in the task 5-way-1-shot, and at most \(C_{30}^{5}\), \(C_{60}^{5}\) in the 5-way-5-shot task. Under the baseline DASTM, in the 5-way-1-shot task, the average top-1 accuracy of the model in NTU-T is 73.86%, and that of NTU-S is 71.26%, with a difference of 2.6%. The difference in 5-way-5-shot task is 3.28%. The model of this study reduces the gap between the two types of tasks in the dataset to 0.94%, 2.38%, which is lower than the original 2.6%, 3.28%. This means that if the model can be continuously optimized, multi-sample performance can be achieved even with one sample. The Kinetics dataset has only 18 joints, so it is not as rich in semantic information as the NTU series dataset. In addition, it is a dataset generated based on video websites and OpenPose, resulting in a lot of noise, which is extremely challenging. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Channels (Input)**} & \multirow{2}{*}{**Channels (Output)**} & **Numbers/Size** & **Number/Size** \\  & & **(CDC-GCN)** & **(CDC-TCN)** \\ \hline
128 & 256 & 3/1*1 & 1/9*1 \\
256 & 256 & 3/1*1 & 1/9*1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed parameters within the encoder.