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SOTA
基于骨骼的动作识别
Skeleton Based Action Recognition On Sysu 3D
Skeleton Based Action Recognition On Sysu 3D
评估指标
Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Accuracy
Paper Title
SGN
86.9%
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
VA-fusion (aug.)
86.7%
View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
EleAtt-GRU (aug.)
85.7%
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks
Local+LGN
83.14%
Learning Latent Global Network for Skeleton-based Action Prediction
Complete GR-GCN
77.9%
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression
VA-LSTM
77.5%
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
DPRL
76.9%
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition
Dynamic Skeletons
75.5%
Jointly learning heterogeneous features for rgb-d activity recognition
ST-LSTM (Tree)
73.4%
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates
0 of 9 row(s) selected.
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HyperAI
HyperAI超神经
首页
算力平台
文档
资讯
论文
教程
数据集
百科
SOTA
LLM 模型天梯
GPU 天梯
顶会
开源项目
全站搜索
关于
服务条款
隐私政策
中文
HyperAI
HyperAI超神经
Toggle Sidebar
全站搜索…
⌘
K
Command Palette
Search for a command to run...
算力平台
首页
SOTA
基于骨骼的动作识别
Skeleton Based Action Recognition On Sysu 3D
Skeleton Based Action Recognition On Sysu 3D
评估指标
Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Accuracy
Paper Title
SGN
86.9%
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
VA-fusion (aug.)
86.7%
View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
EleAtt-GRU (aug.)
85.7%
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks
Local+LGN
83.14%
Learning Latent Global Network for Skeleton-based Action Prediction
Complete GR-GCN
77.9%
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression
VA-LSTM
77.5%
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
DPRL
76.9%
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition
Dynamic Skeletons
75.5%
Jointly learning heterogeneous features for rgb-d activity recognition
ST-LSTM (Tree)
73.4%
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates
0 of 9 row(s) selected.
Previous
Next