Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)

• URL: http://arxiv.org/abs/2411.06553v1
• Date: Sun, 10 Nov 2024 18:28:52 GMT
• Title: Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)
• Authors: Faisal Mehmood, Xin Guo, Enqing Chen, Muhammad Azeem Akbar, Arif Ali Khan, Sami Ullah,
• Abstract summary: Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible.
• Score: 4.822426770727152
• License:
• Abstract: Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data.

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