  Attention is an effective mechanism to improve the deep model capability.
Squeeze-and-Excite (SE) introduces a light-weight attention branch to enhance
the network's representational power. The attention branch is gated using the
Sigmoid function and multiplied by the feature map's trunk branch. It is too
sensitive to coordinate and balance the trunk and attention branches'
contributions. To control the attention branch's influence, we propose a new
attention method, called Shift-and-Balance (SB). Different from
Squeeze-and-Excite, the attention branch is regulated by the learned control
factor to control the balance, then added into the feature map's trunk branch.
Experiments show that Shift-and-Balance attention significantly improves the
accuracy compared to Squeeze-and-Excite when applied in more layers, increasing
more size and capacity of a network. Moreover, Shift-and-Balance attention
achieves better or close accuracy compared to the state-of-art Dynamic
Convolution.
