MFANet

Reported on 4 benchmarks across 2 tasks · 1 paper · 4 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision4 results

HandonDHG-28
Accuracy· 2017-08-10
80.32
best: 93.57 (DSTSA-GCN)
SOTA
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition arXiv:1708.03278
HandonDHG-14
Accuracy· 2017-08-10
84.68
best: 95.83 (e2eET)
SOTA
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition arXiv:1708.03278
Gesture RecognitiononDHG-28
Accuracy· 2017-08-10
80.32
best: 93.57 (DSTSA-GCN)
SOTA
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition arXiv:1708.03278
Gesture RecognitiononDHG-14
Accuracy· 2017-08-10
84.68
best: 95.83 (e2eET)
SOTA
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition arXiv:1708.03278