# GCN-Former: A Method for Action Recognition Using Graph Convolutional Networks and Transformer
## 1 Introduction  
In recent years, with the success of Graph Neural Networks (GNNs) in handling complex data structures, GNNs have demonstrated significant potential in various domains, including but not limited to social network analysis, recommendation systems, and bioinformatics. In particular, Graph Convolutional Networks (GCNs) [1] have significantly enhanced our understanding of graph-structured data by effectively integrating node features and their neighborhood information. However, when dealing with data that exhibit spatiotemporal characteristics, traditional GCN models often struggle to be directlyapplicable, as they primarily focus on static graph structures and overlook the influence of the temporal dimension. This issue is particularly prominent in skeleton-based action recognition. Early methods primarily relied on handcrafted features and traditional machine learning algorithms, such as Support Vector Machines (SVMs) [2] and Hidden Markov Models (HMMs) [3]. Although these methods achieved some success in simpler tasks, their performance is limited when handling complex action sequences, as they struggle to capture the temporal dependencies and spatial structures inherent in the actions. In recent years, the development of deep learning techniques has brought revolutionary changes to skeleton-based action recognition. In particular, the successful application of Convolutional Neural Networks (CNNs) [4,5] and Recurrent Neural Networks (RNNs) [6, 7, 8] has enabled models to automatically learn high-level feature representations from raw skeleton data. However, these methods still have limitations when dealing with graph-structured data, as they are mainly designed for regular grid data. To address this issue, researchers have begun exploring methods to incorporate temporal information into GCNs. [PERSON] et al. [9] proposed Spatiotemporal Graph Convolutional Networks (ST-GCNs) in 2018, which modeled human skeleton sequences in spatial and temporal dimensions for the first time, treating joints as graph vertices and constructing a spatiotemporal graph. However, the fixed graph topology in ST-GCNs does not account for non-physical connections. [PERSON] et al. [10] introduced a directed graph and a weighted adjacency matrix, emphasizing spatial proximity between joints, enhancing the model's ability to focus on relevant relationships. [PERSON] et al. [10] proposed an AS-GCN, incorporating two sub-networks for motion and structural connections, expanding the neighborhood and enriching joint-related cues. [PERSON] et al. [11] proposed a 2s-AGCN, which allows the graph topology to be learned in an end-to-end fashion, improving flexibility and joint interaction learning. [PERSON] et al. [12] further improved the ST-GCN and 2s-AGCN by representing human skeletons with directed acyclic graphs and introducing directed graph neural networks to extract features of joint interactions, skeleton information and their relationships. [PERSON] et al. [13] developed CA-GCN, which enables nodes to consider both local and distant joints, capturing long-range dependencies without multiple layers, simplifying the network. [PERSON] et al. [14] proposed a Shift-GCN, introducing a spatial shift operation to reduce complexity and improve performance for long time series. [PERSON] et al. proposed InfoGCN [15] and InfoGCN++ [16], which optimized the graph convolution process using information theory, enhancing robustness and accuracy in complex scenarios. [PERSON] et al. [17] introduced HD-GCN, employing hierarchical decomposition to optimally configure different GCN components and improve overall model performance. [PERSON] et al. [18] developed CTRGCN, which fine-tuned the channel topology of GCNs to enhance adaptability for complex action recognition tasks. [PERSON] et al. [19] proposed BlockGCN, redefining the graph topology to boost generalization and adaptability, further advancing GCN performance in action recognition. These contributions collectively drive the evolution of GCNs in skeleton-based action recognition.