# Reconstructed Prototype Network Combined with CDC-TAGCN for Few-Shot Action Recognition
## 2 Related Work  
In this section, relevant work is presented from two main aspects: few-shot action recognition and spatiotemporal encoders. **Few-Shot Action Recognition:** Few-shot learning is an application of meta-learning [11] in supervised learning, aiming to enable models to learn how to learn. It has been applied in various domains, such as face recognition [12], fault detection [13; 14], and object recognition [15; 16]. [PERSON] et al. [17] were among the early adopters of the few-shot concept in action recognition. They introduced CMN, which is based on memory networks. CMN uses multiple vectors to represent videos and presents a series of hidden saliency descriptors as constituent keys in storage. Although a layered structure improves efficiency, memory networks [17; 18] consume significant storage space. As research progressed, relation networks [19], matching networks [20; 21; 22], and prototype networks [2; 21; 23] were gradually introduced. [PERSON] et al. [19] used a hybrid relation module to capture relationships between different episodic tasks and utilized the mean hausdorff metric to measure the distance between query and support samples. In one study by [PERSON] [21], an LSTM with a matching network framework was employed. This framework effectively extracted features from video sequences, and with the help of the matching network, query samples could find the nearest corresponding support samples. Relation networks and matching networks need to calculate all sample relationships, so the computational cost is very high. In [PERSON] et al. 's research, metric learning was divided into matching networks and prototype networks. The experiments demonstrate that prototype networks are better at focusing on class-specific features, and computational cost is significantly reduced. TAEN [23] feeds trajectory features into a framework that combines RepMet with prototype networks, and experiments demonstrate the strong generalization capabilities of prototype networks. The aforementioned studies are all based on video data, while recent research combines few-shot action recognition with skeleton data. In the latest DASTM study, spatial-temporal encoding was combined with prototype networks, and the more challenging Kinect-based and NTU-based datasets were introduced. **Spatiotemporal Encoder:** The action dataset contains information in two dimensions: the topological relationships of joints in space and the temporal changes in joints. Early handcrafted features have been proven to lack generality and are only suitable for specific datasets [24]. Existing research has employed CNNs [5; 6], RNNs [4], and LSTMs [7; 21]. However, it is difficult for those single-step convolutional encoders to fuse temporal and spatial features. [PERSON] first introduced the concept of graph neural networks (GNNs) [25]. Inspired by GNNs, [PERSON] et al. [26] proposed spatiotemporal graph convolutional networks (ST-GCN) and applied them to skeleton-based action recognition, gaining significant attention. ST-GCN employs a two-step convolution: GCN to extract spatial features and TCN to extract temporal features, allowing it to handle non-Euclidean skeletal sequences. Subsequent work has built upon this foundation. [PERSON] et al. [26] argued that different joints exhibit varying changes in various actions, and their topological relationships should not be static. They used two convolution kernels of different sizes to calculate the similarity between joints, forming an additional matrix representing the topological relationships. [PERSON] et al. [27] believed that different channels might have different topological relationships and aimed to learn appropriate matrices while minimizing computational complexity automatically. Most research efforts have focused on improving the GCN module. However, this study points out limitations in feature extraction within the TCN module. The temporal convolution kernel of PYSKL [28] captures features in a multi-branch structure but only captures surface-level features.