  Attention mechanism has gained great success in vision recognition. Many
works are devoted to improving the effectiveness of attention mechanism, which
finely design the structure of the attention operator. These works need lots of
experiments to pick out the optimal settings when scenarios change, which
consumes a lot of time and computational resources. In addition, a neural
network often contains many network layers, and most studies often use the same
attention module to enhance different network layers, which hinders the further
improvement of the performance of the self-attention mechanism. To address the
above problems, we propose a self-attention module SEM. Based on the input
information of the attention module and alternative attention operators, SEM
can automatically decide to select and integrate attention operators to compute
attention maps. The effectiveness of SEM is demonstrated by extensive
experiments on widely used benchmark datasets and popular self-attention
networks.
