  A variety of attention mechanisms have been studied to improve the
performance of various computer vision tasks. However, the prior methods
overlooked the significance of retaining the information on both channel and
spatial aspects to enhance the cross-dimension interactions. Therefore, we
propose a global attention mechanism that boosts the performance of deep neural
networks by reducing information reduction and magnifying the global
interactive representations. We introduce 3D-permutation with
multilayer-perceptron for channel attention alongside a convolutional spatial
attention submodule. The evaluation of the proposed mechanism for the image
classification task on CIFAR-100 and ImageNet-1K indicates that our method
stably outperforms several recent attention mechanisms with both ResNet and
lightweight MobileNet.
