HyperAI

Skeleton Based Action Recognition On Ntu Rgbd 1

Metrics

Accuracy (Cross-Setup)
Accuracy (Cross-Subject)
GFLOPS per prediction

Results

Performance results of various models on this benchmark

Model Name
Accuracy (Cross-Setup)
Accuracy (Cross-Subject)
GFLOPS per prediction
Paper TitleRepository
S-TR (1-stream)81.880.216.2Continual Spatio-Temporal Graph Convolutional Networks
Dynamic Skeletons54.7%50.8%-Jointly learning heterogeneous features for rgb-d activity recognition-
DG-STGCN91.389.6-DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
JMDA (based on Skeleton MixFormer)91.990.9-Joint Mixing Data Augmentation for Skeleton-based Action Recognition
ST-GCN (1-stream)-7916.73Continual Spatio-Temporal Graph Convolutional Networks
3s-HYSP86.384.5-HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations
AGCN (1-stream)80.779.718.69Continual Spatio-Temporal Graph Convolutional Networks
SkeletonGCL (based on CTR-GCN)91.089.5-Graph Contrastive Learning for Skeleton-based Action Recognition
EfficientGCN-B084.385.9-Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
TSRJI (Late Fusion) + HCN62.8%67.9%-Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints
Two-Stream Attention LSTM63.3%61.2%-Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks-
EfficientGCN-B489.188.7-Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
Spatio-Temporal LSTM57.9%55.7%-Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition-
GVFE + AS-GCN with DH-TCN79.8%78.3%-Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatial-Temporal Graph Convolutional Network for Action Recognition-
Multi-Task Learning Network57.9%58.4%-A New Representation of Skeleton Sequences for 3D Action Recognition-
TCA-GCN90.889.4-Skeleton-based Action Recognition via Temporal-Channel Aggregation
DualHead-Net89.388.2-Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition
SkateFormer91.489.8-SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition
InfoGCN91.289.8-InfoGCN: Representation Learning for Human Skeleton-Based Action Recognition
Gimme Signals (Skeleton, AIS)71.6%70.8%-Gimme Signals: Discriminative signal encoding for multimodal activity recognition
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