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A variety of attention mechanisms have been studied to improve theperformance of various computer vision tasks. However, the prior methodsoverlooked the significance of retaining the information on both channel andspatial aspects to enhance the cross-dimension interactions. Therefore, wepropose a global attention mechanism that boosts the performance of deep neuralnetworks by reducing information reduction and magnifying the globalinteractive representations. We introduce 3D-permutation withmultilayer-perceptron for channel attention alongside a convolutional spatialattention submodule. The evaluation of the proposed mechanism for the imageclassification task on CIFAR-100 and ImageNet-1K indicates that our methodstably outperforms several recent attention mechanisms with both ResNet andlightweight MobileNet.

Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions