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

Published 10 Nov 2024 in cs.CV | (2411.06553v1)

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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