  Extensive research works demonstrate that the attention mechanism in
convolutional neural networks (CNNs) effectively improves accuracy.
Nevertheless, few works design attention mechanisms using large receptive
fields. In this work, we propose a novel attention method named Rega-net to
increase CNN accuracy by enlarging the receptive field. Inspired by the
mechanism of the human retina, we design convolutional kernels to resemble the
non-uniformly distributed structure of the human retina. Then, we sample
variable-resolution values in the Gabor function distribution and fill these
values in retina-like kernels. This distribution allows essential features to
be more visible in the center position of the receptive field. We further
design an attention module including these retina-like kernels. Experiments
demonstrate that our Rega-Net achieves 79.96% top-1 accuracy on ImageNet-1K
classification and 43.1% mAP on COCO2017 object detection. The mAP of the
Rega-Net increased by up to 3.5% compared to baseline networks.
