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Found 7 papers, 5 papers with code
Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
One essential problem in skeleton-based action recognition is how to extract discriminative features over all skeleton joints.
Ranked #17 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition
However, the complexity of the State-Of-The-Art (SOTA) models of this task tends to be exceedingly sophisticated and over-parameterized, where the low efficiency in model training and inference has obstructed the development in the field, especially for large-scale action datasets.
Ranked #25 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition
More crucially, on the synthetic occlusion and jittering datasets, the performance deterioration due to the occluded and disturbed joints can be significantly alleviated by utilizing the proposed RA-GCN.
Ranked #43 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Light Pose Calibration for Camera-light Vision Systems
Illuminating a scene with artificial light is a prerequisite for seeing in dark environments.
Deep Sea Robotic Imaging Simulator
Nowadays underwater vision systems are being widely applied in ocean research.
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons
To enhance the robustness of action recognition models to incomplete skeletons, we propose a multi-stream graph convolutional network (GCN) for exploring sufficient discriminative features distributed over all skeleton joints.
Ranked #74 on
Skeleton Based Action Recognition
on NTU RGB+D
Mexican Hat Wavelet Kernel ELM for Multiclass Classification
Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems.
