2s-GATCN: Two-Stream Graph Attentional Convolutional Networks for Skeleton-Based Action Recognition

Zhou, S. Kevin; Chen, Ranran; Jiang, Xueqin; Pan, Feng

doi:10.3390/electronics12071711

Cited by 6 publications

(1 citation statement)

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“…In their recent work, Song et al [ 25 ] proposed the Spatial Temporal Joint Attention module, which allows key joints to be found in a spatial and temporal sequence to better achieve efficient topology modeling. Zhou et al [ 26 ] introduced a graph attention block based on Convolutional Block Attention Module (CBAM), which is used to calculate the semantic correlation between any two joints. TCA-GCN [ 27 ] used an MS-CAM attention fusion mechanism to solve the problem of the contextual aggregation of skeletal features.…”

Section: Related Workmentioning

confidence: 99%

Multi-Modality Adaptive Feature Fusion Graph Convolutional Network for Skeleton-Based Action Recognition

Zhang

et al. 2023

Sensors

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Graph convolutional networks are widely used in skeleton-based action recognition because of their good fitting ability to non-Euclidean data. While conventional multi-scale temporal convolution uses several fixed-size convolution kernels or dilation rates at each layer of the network, we argue that different layers and datasets require different receptive fields. We use multi-scale adaptive convolution kernels and dilation rates to optimize traditional multi-scale temporal convolution with a simple and effective self attention mechanism, allowing different network layers to adaptively select convolution kernels of different sizes and dilation rates instead of being fixed and unchanged. Besides, the effective receptive field of the simple residual connection is not large, and there is a great deal of redundancy in the deep residual network, which will lead to the loss of context when aggregating spatio-temporal information. This article introduces a feature fusion mechanism that replaces the residual connection between initial features and temporal module outputs, effectively solving the problems of context aggregation and initial feature fusion. We propose a multi-modality adaptive feature fusion framework (MMAFF) to simultaneously increase the receptive field in both spatial and temporal dimensions. Concretely, we input the features extracted by the spatial module into the adaptive temporal fusion module to simultaneously extract multi-scale skeleton features in both spatial and temporal parts. In addition, based on the current multi-stream approach, we use the limb stream to uniformly process correlated data from multiple modalities. Extensive experiments show that our model obtains competitive results with state-of-the-art methods on the NTU-RGB+D 60 and NTU-RGB+D 120 datasets.

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Section: Related Workmentioning

confidence: 99%