# GCN-Former: A Method for Action Recognition Using Graph Convolutional Networks and Transformer
## 1 Introduction
Despite the significant advancements of GCNs in handling graph-structured data, there are still limitations when processing long temporal sequences. Meanwhile, the Transformer architecture [20] has seen significant success in Natural Language Processing (NLP). Its core advantage lies in the self-attention mechanism, which efficiently handles long sequence data and excels at modeling long-range dependencies. Compared to traditional RNNs and CNNs, the Transformer not only avoids the vanishing and exploding gradient problem but also supports parallel computation, significantly enhancing training efficiency. This makes the Transformer a potential solution to address the limitations of GCNs and TCNs. In skeleton-based action recognition, some studies have begun to explore the application of the Transformer to this task. For example, [PERSON] et al. [21] proposed a Spatial-Temporal Transformer network (ST-TR), a model that combines spatial and temporal self-attention mechanisms. ST-TR understands the interactions between different body parts within a frame through the spatial self-attention module (SSA) and models the association between frames through the temporal self-attention module (TSA). [PERSON] et al. [22] proposed a Spatial-Temporal Specialized Transformer (STST), a Transformer encoder specifically designed for skeleton-based action recognition. An STST models the pose of each skeleton frame and the action of the entire time span, respectively, through spatial and temporal self-attention mechanisms. [PERSON] et al. [23] proposed a model, FG-STFormer, that combines focal and global spatiotemporal transformers. The FG-STFormer models key joints and overall motion patterns through spatial transformers coupled with focal joints and global parts, as well as focal and global temporal transformers. [PERSON] et al. [24] proposed TAR-128, a sparse Transformer for skeleton recognition, incorporating a sparse attention module that reduces redundant computations by focusing on neighboring joints and leveraging logical relations within body parts. [PERSON] et al. [25] proposed Hyperformer, advancing Transformer-based models by incorporating skeletal structure through relative position embeddings and a Hypergraph Self-Attention (HyperSA) mechanism to capture high-order kinematic dependencies, outperforming graph-based models in accuracy and efficiency. [PERSON] et al. [26] proposed the Interaction Graph Transformer (IGFFormer), which uses a Graph Interaction Multi-Head Self-Attention (GI-MSA) module to model interactions between body parts of multiple subjects, along with a semantic partitioning module (SPM) for better handling of interactive actions. [PERSON] et al. [27] introduced SkeleTR, a framework that uses short skeleton sequences to reduce association errors, with a hybrid pooling module for efficiency. [PERSON] et al. [28] proposed the Interactive Spatiotemporal Token Attention Network (ISTA-Net), which uses interactive spatiotemporal tokens (ISTs) to capture spatial, temporal, and interactive relationships. These contributions highlight the ongoing advancements in integrating Transformers for skeleton-based action recognition, each focusing on optimizing spatiotemporal modeling, interaction capture, and computational efficiency. Building upon this, the present work proposes an innovative spatiotemporal graph convolutional network by replacing the original TCN module with a Transformer, thus creating a more flexible and efficient model. Specifically, we designed a Transformer Block temporal encoder based on the self-attention mechanism to capture the dynamic features of key points over time while retaining the spatial aggregation capability of GCNs. This ensures that the model can comprehensively consider both temporal and spatial information. In addition, through experiments on multiple benchmark datasets, this paper aims to demonstrate that the proposed model not only outperforms existing methods in prediction accuracy but also exhibits higher efficiency and robustness in handling complex spatiotemporal data.