# Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition
## 2 Related Work
### Graph Convolutional Network and Attention Mechanism  
Graph convolutional network: Recently, GCN has been introduced to skeleton-based action recognition and has shown good performance. The principle of constructing GCNs on graphs is mainly divided into spatial methods [31; 32] and spectral methods [33; 34]. The spatial methods directly perform convolution operation on the graph node and its neighbor nodes to extract features based on the design rules. Additionally, the spectral methods use the eigenvalues and eigenvectors of the graph Laplacian to perform graph convolution in the frequency domain. Most of the existing GCN-based methods [13; 15; 16; 35; 36] focus on the design of graph topology.  
[PERSON] et al. [13] first proposed to utilize GCN to model the skeleton sequence. [PERSON] et al. [15] proposed a two-stream adaptive graph convolutional network, which parameterizes the graph structure of the skeleton data, inputs it into the network and learns and updates features together with the model to increase the flexibility of GCN. [PERSON] et al. [16] introduced the encoder-decoder structure to capture the implicit joint correlation between specific actions. It also expands the existing skeleton graph to express higher-order dependencies and combines actional and structural links to learn spatial and temporal features.  
[PERSON] et al. [14] proposed a motif-based GCN, which models the dependency between physically connected and disconnected nodes to extract high-order spatial information and proposed a variable temporal dense block to capture temporal information in different ranges. [PERSON] et al. [37] proposed to use a lightweight shift operation to replace the 2D convolution operation, achieving excellent performance at a small computational cost. [PERSON] et al. [35] introduced a lightweight and effective Context-encoding Network (CeN) to explore the context encoding information and added it to the GCN to automatically learn the skeleton topology. [PERSON] et al. [36] applied the neural structure search and replaces the predefined graph structure with a dynamic graph structure to explore the generation mechanism of different graphs under different semantics.  
Attention mechanism: The attention mechanism can help neural networks pay attention to the more important feature parts of the input information. Many researchers have added the attention mechanism to the action recognition tasks to better model the spatio-temporal graph structure. [PERSON] et al. [17] focused on the joint information in the frame through the global context memory cell and introduced a recurrent attention mechanism, which solves the problem of the original LSTM network not having an explicit attention mechanism. Similarly, [PERSON] et al. [18] also proposed to add Element-wise-Attention Gate (EleAttG) to RNN, which enables RNN neurons to have the attention ability to adaptively modulate the input. [PERSON] et al. [14] employed the non-local block to capture dependencies, which enhances the ability of the network to extract global temporal features. [PERSON] et al. [38] proposed a spatial and temporal attention mask, which reuses important spatio-temporal features and enables GCN to aggregate global context information of the skeleton sequence.