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A module selection-based approach for efficient skeleton human action recognition
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AI
DOI：10.1016/j.displa.2025.103233.png)
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Abstract
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• Adaptive module selection strategy for skeleton-based action recognition is proposed. • Conventional methods employ fixed network whereas our approach is adaptive. • Introduces sparsity loss to balance accuracy and efficiency. • Reduces inference time by up to 3×while maintaining the performance. • Compatible with multiple SOTA models.
Keywords:
Graph convolutional networks
Skeleton human action recognition
Human–computer interaction
Efficient network
Dynamic network
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