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.
翻译:许多工作都致力于提高关注机制的效能,这种机制精细设计关注经营人的结构,这些工作需要大量实验,以便在情景变化时选择最佳环境,这种变化耗费大量时间和计算资源。此外,神经网络往往包含许多网络层,大多数研究往往使用同样的关注模块来增强不同的网络层,这妨碍了进一步改进自控机制的绩效。为了解决上述问题,我们提议了一个自发的SEM模块。根据关注模块和替代性关注运营人的投入信息,SEM可以自动决定选择和整合关注运营人来绘制关注地图。关于广泛使用的基准数据集和大众自控网络的广泛实验证明了SEM的有效性。