iPay fuses RGB and skeleton expert streams via dual-attention and a prior-driven Spatial Difference Discriminator to reach 83.45% accuracy on 500+ real-world payment clips from onboard transit cameras.
Infogcn: Representation learning for human skeleton-based action recognition
3 Pith papers cite this work. Polarity classification is still indexing.
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Higher temporal resolution in video significantly improves zero-shot semantic understanding of high-speed human actions like kendo.
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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iPay: Integrated Payment Action Recognition via Multimodal Networks and Adaptive Spatial Prior Learning
iPay fuses RGB and skeleton expert streams via dual-attention and a prior-driven Spatial Difference Discriminator to reach 83.45% accuracy on 500+ real-world payment clips from onboard transit cameras.
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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.