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Spatial-temporal attention augmented graph convolution method based on human posture response
DOI:10.1016/j.sna.2025.117017.png)
Abstract
En 中文
• Novel ST-E-GCN architecture enhances spatiotemporal feature learning through channel-wise attention mechanisms. • Real-time pose-responsive lighting control achieves about 100 ms latency in stage performance synchronization. • 4.87 % accuracy improvement on complex dance datasets compared to baseline ST-GCN models. • Hardware-software co-design integrates embedded systems with adaptive DMX512 protocol parsing.
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IF:
4.9
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1.5W
Citations:
3.3W
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