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SOTA
Handgesten-Erkennung
Hand Gesture Recognition On Dhg 14
Hand Gesture Recognition On Dhg 14
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Accuracy
Paper Title
Repository
Parallel-Conv
91.28
Deep Learning for Hand Gesture Recognition on Skeletal Data
MFANet
84.68
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition
-
DD-Net
94.6
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
-
SL-fusion-Maximum
85.36
CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition
-
TD-GCN
93.9
Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture Recognition
-
e2eET
95.83
Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN
-
SL-fusion-Average
85.46
CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition
-
FPPR-PCD
92.0
Fusing Posture and Position Representations for Point Cloud-Based Hand Gesture Recognition
DG-STA
91.9
Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention
-
FL-fusion-Concat
81.86
CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition
-
Ensemble of Models
86.11
An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition
-
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Hand Gesture Recognition On Dhg 14 | SOTA | HyperAI