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Multi-Scale Mixed Dense Graph Convolution Network for Skeleton-Based Action Recognition
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DOI：10.1109/ACCESS.2020.3049029.png)
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摘要
En 中文
In skeleton-based action recognition, the approaches based on graph convolutional networks(GCN) have achieved remarkable performance by modeling spatial-temporal graphs to explore the physical dependencies between body joints. However, these methods mostly apply hierarchical GCNs to aggregate wider-range neighborhood information, which makes joint features be weakened during long diffusion. In this paper, we design a multi-scale mixed dense graph convolutional network (MMDGCN) to overcome both shortcomings. We propose a dense graph convolution operation to enhance the local context information of joints, and then the spatial and temporal attention modules with a larger receptive field are introduced to help the model strengthen the discriminative features to adaptively refine the intermediate feature maps. We also design a multi-scale mixed temporal convolution module, which provides a flexible temporal graph through the combination of different scale convolution kernels. Extensive experiments on the three real-world datasets (NTU-RGB+D, NTU-RGB+D120 and Kinetics) demonstrate that the performance of the proposed MMDGCN in skeleton-based action recognition.
Keyword:
Convolution
Skeleton
Feature extraction
Joints
Data models
Context modeling
Recurrent neural networks
Dense graph convolution
spatial and temporal attention module
multi-scale mixed temporal convolution
skeleton-based action recognition
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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