  For the two-stream style methods in action recognition, fusing the two
streams' predictions is always by the weighted averaging scheme. This fusion
method with fixed weights lacks of pertinence to different action videos and
always needs trial and error on the validation set. In order to enhance the
adaptability of two-stream ConvNets and improve its performance, an end-to-end
trainable gated fusion method, namely gating ConvNet, for the two-stream
ConvNets is proposed in this paper based on the MoE (Mixture of Experts)
theory. The gating ConvNet takes the combination of feature maps from the same
layer of the spatial and the temporal nets as input and adopts ReLU (Rectified
Linear Unit) as the gating output activation function. To reduce the
over-fitting of gating ConvNet caused by the redundancy of parameters, a new
multi-task learning method is designed, which jointly learns the gating fusion
weights for the two streams and learns the gating ConvNet for action
classification. With our gated fusion method and multi-task learning approach,
a high accuracy of 94.5% is achieved on the dataset UCF101.
