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.

翻译：大量研究证明，卷积神经网络中的注意力机制能有效提升精度。然而，现有工作鲜有利用大感受野设计注意力机制。本文提出一种名为Rega-Net的新型注意力方法，通过扩大感受野提升CNN精度。受人类视网膜机制启发，我们设计出模拟人类视网膜非均匀分布结构的卷积核。随后在Gabor函数分布中采样可变分辨率数值，并将这些数值填充至类视网膜核中。这种分布使得感受野中心位置的关键特征更易被感知。我们进一步设计了包含这些类视网膜核的注意力模块。实验表明，Rega-Net在ImageNet-1K分类任务上达到79.96%的Top-1准确率，在COCO2017目标检测任务上达到43.1%的mAP。与基线网络相比，Rega-Net的mAP最高提升了3.5%。