Large-scale Isolated Gesture Recognition using pyramidal 3D convolutional networks

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RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017, positive): “[177] fused RGB and depth in a pyramidal 3D convolutional network based on C3D [130] for gesture recognition.”
Large-scale Isolated Gesture Recognition using Convolutional Neural Networks (2016, methodology): “mission with those of other teams. Our team secures the second place in the ICPR ChaLearn LAP challenge 2016. Rank Team Recognition rate r 1 FLiXT [38] 56.8968% 2 AMRL (ours) 55.5733% 3 XDETVP-TRIMPS [39] 50.9329% 4 ICT NHCI 46.8027% 5 XJTUfx 43.9164% 6 TARDIS 40.1531% 7 NTUST 20.3317% sequences int”
Large-scale gesture recognition with a fusion of RGB-D data based on the C3D model (2016, methodology): “The final score of top 7 results, including [24] and [25] (which wins the second and third place) and the baseline method [2] on testing data are shown in Fig.”
Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM (2017, methodology): “[7] embedded pyramidal input and pyramidal fusion strategies into the C3D model for gesture recognition.”
Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks (2018, methodology): “Two-stream RNNs [60] ﬁrst adopted R-CNN to extract the hand and then conducted temporal segmentation.”