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：本文提出在卷积神经网络中引入空间偏置以学习全局知识，无需自注意力机制。由于感受野受限，传统卷积神经网络难以捕获长距离依赖关系。非局部神经网络虽致力于学习全局知识，但因自注意力操作不可避免地导致网络设计过于繁重。为此，我们提出一种快速轻量的空间偏置方法，能够在无需自注意力的条件下高效编码全局知识。该空间偏置通过堆叠于特征图并与之共同卷积，从而调整卷积特征的空间结构，使得我们能够以极少的额外资源直接在卷积层学习全局知识。由于采用无需注意力的非局部方法，本方法在显著提升神经网络性能的同时保持极快的运算速度与轻量性。与非局部神经网络相比，空间偏置方法参数数量减少约10倍，在极低资源消耗下实现了1.6~3.3倍的吞吐量提升，且性能相当。此外，空间偏置可与传统非局部神经网络结合使用，进一步提升骨干网络性能。实验表明，空间偏置在ImageNet-1K和CIFAR100数据集上分别将分类准确率提升+0.79%和+1.5%。我们同时在MS-COCO和ADE20K数据集上验证了该方法在目标检测与语义分割等下游任务中的有效性。