Rega-Net: Retina Gabor Attention for Deep Convolutional Neural Networks

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. To the best of our knowledge, increasing the receptive field of the CNN requires increasing the size of the convolution kernel, which also increases the number of parameters. For solving this problem, we design convolutional kernels to resemble the nonuniformly distributed structure inspired by the mechanism 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 for classification and 43.1% mAP on COCO2017 for object detection. The mAP of the Rega-Net increased by up to 3.5% compared to baseline networks.