  Hand gesture recognition is a strenuous task to solve in videos. In this
paper, we use a 3D residual attention network which is trained end to end for
hand gesture recognition. Based on the stacked multiple attention blocks, we
build a 3D network which generates different features at each attention block.
Our 3D attention based residual network (Res3ATN) can be built and extended to
very deep layers. Using this network, an extensive analysis is performed on
other 3D networks based on three publicly available datasets. The Res3ATN
network performance is compared to C3D, ResNet-10, and ResNext-101 networks. We
also study and evaluate our baseline network with different number of attention
blocks. The comparison shows that the 3D residual attention network with 3
attention blocks is robust in attention learning and is able to classify the
gestures with better accuracy, thus outperforming existing networks.
