Higher temporal resolution in video significantly improves zero-shot semantic understanding of high-speed human actions like kendo.
Marker-less kendo motion prediction using high-speed dual-camera system and lstm method,
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SASI combines skeleton-based graph convolutions with sub-action semantics for improved early action recognition on the BABEL dataset.
citing papers explorer
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High-Speed Vision Improves Zero-Shot Semantic Understanding of Human Actions
Higher temporal resolution in video significantly improves zero-shot semantic understanding of high-speed human actions like kendo.
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SASI: Leveraging Sub-Action Semantics for Robust Early Action Recognition in Human-Robot Interaction
SASI combines skeleton-based graph convolutions with sub-action semantics for improved early action recognition on the BABEL dataset.