# 3D-STARNET: Spatial-Temporal Attention Residual Network for Robust Action Recognition
## 2 Related Work  
### GCN-Based Action Recognition  
Due to the significant advantages of the graph convolutional network (GCN) in processing graph data, it is widely used in the field of skeleton-based behavior recognition. It converts human skeleton sequences into spatiotemporal graphs and can simulate the complex spatiotemporal structure of human joints and correlation; therefore, GCN-based behavior recognition has become a popular research area [8]. [PERSON] et al. [9] proposed a new dynamic skeleton model ST-GCN that can automatically learn temporal and spatial patterns from data. However, it ignores the implicit correlation between skeletons. Therefore, [PERSON] et al. [10] proposed the action-structure graph convolutional network (AS-GCN), which stacks graph convolution and temporal convolution as basic building blocks to learn the spatial and temporal features of behavioral actions. While performing action recognition, it can also predict future actions to help capture more detailed action patterns through self-supervision. In order to preserve the loss implicit joint correlations, [PERSON] et al. [11] proposed an automatically designed GCN, explored the spatiotemporal correlations between nodes, and constructed a search space with multiple dynamic graph modules. They also proposed corresponding sampling and memory-efficient evolution strategies to search the space. The resulting architecture verified the effectiveness of high-order approximation and layer-by-layer dynamic graph modules. The G3D module proposed by [PERSON] et al. [12] utilizes dense cross-spacetime edges as skip connections to directly propagate information in the space-time graph, and proposes a multi-scale aggregation scheme to reveal the importance of nodes in different neighborhoods for effective long-range modeling.  
[PERSON] et al. [13] designed a novel correlation-driven joint-skeleton fusion graph convolutional network (CD-JBF-GCN) as an encoder and used a pose prediction head as a decoder to achieve semi-supervised learning. The motion transfer between the joint stream and the bone stream can be explored, thereby promoting the two streams to learn more discriminative feature representations. In order to improve the flexibility of GCN in modeling temporal information, [PERSON] et al. [14] proposed a temporal decoupled graph convolutional network (TD-GCN), which first extracts high-level spatiotemporal features from skeleton data, and then calculates the channel-dependent and time-dependent adjacency matrices corresponding to different channels and frames to capture the spatiotemporal dependencies between skeleton joints. Finally, in order to fuse the topological information of adjacent skeleton joints, the spatiotemporal features of skeleton joints are fused based on the channel-dependent and time-dependent adjacency matrices. [PERSON] et al. [15] proposed a dynamic dense graph convolutional network (DD-GCN) that uses 4D adjacency modeling to construct a dense graph as a comprehensive representation of motion sequences at different levels of abstraction. Although a large number of studies have achieved promising results, GCNs are sensitive to noise or irregular connections between skeleton key points, which may affect the recognition accuracy, especially in complex actions.