Spatial Bias for Attention-free Non-local Neural Networks
This addresses the computational inefficiency of non-local neural networks for computer vision tasks, offering a lightweight alternative for researchers and practitioners, though it is incremental as it builds on existing non-local methods.
The paper tackles the problem of learning long-range dependencies in convolutional neural networks without the heavy computational cost of self-attention, proposing a spatial bias method that achieves comparable performance with 10 times fewer parameters and 1.6-3.3 times more throughput, improving classification accuracy by +0.79% on ImageNet-1K and +1.5% on CIFAR100.
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.