In this paper, we introduce the spatial bias to learn global knowledge without self-attention in convolutional neural networks. Owing to the limited receptive field, conventional convolutional neural networks suffer from learning long-range dependencies. Non-local neural networks have struggled to learn global knowledge, but unavoidably have too heavy a network design due to the self-attention operation. Therefore, we propose a fast and lightweight spatial bias that efficiently encodes global knowledge without self-attention on convolutional neural networks. Spatial bias is stacked on the feature map and convolved together to adjust the spatial structure of the convolutional features. Therefore, we learn the global knowledge on the convolution layer directly with very few additional resources. Our method is very fast and lightweight due to the attention-free non-local method while improving the performance of neural networks considerably. Compared to non-local neural networks, the spatial bias use about 10 times fewer parameters while achieving comparable performance with 1.6 ~ 3.3 times more throughput on a very little budget. Furthermore, the spatial bias can be used with conventional non-local neural networks to further improve the performance of the backbone model. We show that the spatial bias achieves competitive performance that improves the classification accuracy by +0.79% and +1.5% on ImageNet-1K and cifar100 datasets. Additionally, we validate our method on the MS-COCO and ADE20K datasets for downstream tasks involving object detection and semantic segmentation.
翻译:在本文中,我们引入了空间偏差,以学习全球知识而无需在进化神经网络中自我关注。由于接收场有限,常规进化神经网络在学习远程依赖性方面受到学习的制约。非本地神经网络在学习全球知识方面困难重重,但由于自我关注操作,因此不可避免地过于沉重的网络设计。因此,我们提出快速和轻度的空间偏差,这种偏差有效地将全球知识编码而无需对进化神经网络进行自我关注。空间偏差堆积在地貌地图上,并结合在一起,以调整进化特征的空间结构。因此,我们直接用很少的额外资源学习关于进化层的全球知识。由于没有注意的非本地方法,我们的方法非常快速和轻重,同时大幅改进神经网络的性能。与非本地神经网络相比,空间偏差使用大约10倍的参数,同时以1.6至3.3倍的倍的可比较性能来调整进化预算。此外,空间偏差可以用常规的非本地神经网络进行空间偏差,通过常规的非本地网络来改进我们的数据偏差的正比度,从而改进了我们的数据校正标的成绩。</s>