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Unsupervised Spatial-Temporal Feature Enrichment and Fidelity Preservation Network for Skeleton based Action Recognition
To address this problem, the overfitting mechanism behind the unsupervised learning for skeleton based action recognition is first investigated.
A Two-stream Neural Network for Pose-based Hand Gesture Recognition
The self-attention based graph convolutional network has a dynamic self-attention mechanism to adaptively exploit the relationships of all hand joints in addition to the fixed topology and local feature extraction in the GCN.
A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition
Smartphone sensors based human activity recognition is attracting increasing interests nowadays with the popularization of smartphones.
Skeleton-based Action Recognition Using LSTM and CNN
Recent methods based on 3D skeleton data have achieved outstanding performance due to its conciseness, robustness, and view-independent representation.
Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks
Convolutional Neural Networks (ConvNets) have recently shown promising performance in many computer vision tasks, especially image-based recognition.
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Skeleton Based Action Recognition
on Gaming 3D (G3D)
