  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.
