论文标题
Rega-net：视网膜Gabor对深卷积神经网络的关注
Rega-Net:Retina Gabor Attention for Deep Convolutional Neural Networks
论文作者
论文摘要
广泛的研究工作表明，卷积神经网络（CNN）的注意力机制有效地提高了准确性。然而，很少有作品使用大型接受场来设计注意机制。在这项工作中，我们提出了一种名为Rega-net的新型注意方法，以通过扩大接受场来提高CNN的精度。受到人类视网膜机制的启发，我们设计了卷积内核，以类似于人类视网膜的不均匀分布结构。然后，我们在Gabor函数分布中采样可变分辨率值，并在类似视网膜的内核中填充这些值。该分布允许在接受场的中心位置更明显的基本特征。我们进一步设计了一个关注模块，包括这些类似视网膜的内核。实验表明，我们的Rega-NET在Imagenet-1K分类上达到了79.96％的TOP-1准确性，而COCO2017对象检测的映射为43.1％。与基线网络相比，Rega-NET的地图增加了3.5％。
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.
