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Published in IET Image Processing, 2023
This paper proposes DenseGCN, a novel multi-level and multi-temporal graph convolutional network for skeleton-based action recognition that effectively captures both spatial and temporal dependencies in human motion sequences.
Recommended citation: Yu, C., et al. (2023). "DenseGCN: A multi-level and multi-temporal graph convolutional network for action recognition." IET Image Processing. 17(11), 3299-3312.
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Published in IEEE Conference, 2024
This paper presents a novel self-supervised approach for human keypoint detection using pressure maps, achieving improved generalization and computational efficiency across different datasets without requiring manual annotations.
Recommended citation: Yu, C., et al. (2024). "A Self-Supervised Pressure Map Human Keypoint Detection Approach: Optimizing Generalization and Computational Efficiency Across Datasets." IEEE Conference Proceedings. DOI: 10.1109/10447055.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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