  Light-weight convolutional neural networks (CNNs) are specially designed for
applications on mobile devices with faster inference speed. The convolutional
operation can only capture local information in a window region, which prevents
performance from being further improved. Introducing self-attention into
convolution can capture global information well, but it will largely encumber
the actual speed. In this paper, we propose a hardware-friendly attention
mechanism (dubbed DFC attention) and then present a new GhostNetV2 architecture
for mobile applications. The proposed DFC attention is constructed based on
fully-connected layers, which can not only execute fast on common hardware but
also capture the dependence between long-range pixels. We further revisit the
expressiveness bottleneck in previous GhostNet and propose to enhance expanded
features produced by cheap operations with DFC attention, so that a GhostNetV2
block can aggregate local and long-range information simultaneously. Extensive
experiments demonstrate the superiority of GhostNetV2 over existing
architectures. For example, it achieves 75.3% top-1 accuracy on ImageNet with
167M FLOPs, significantly suppressing GhostNetV1 (74.5%) with a similar
computational cost. The source code will be available at
https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/ghostnetv2_pytorch
and https://gitee.com/mindspore/models/tree/master/research/cv/ghostnetv2.
