  Spatial attention has been widely used to improve the performance of
convolutional neural networks. However, it has certain limitations. In this
paper, we propose a new perspective on the effectiveness of spatial attention,
which is that the spatial attention mechanism essentially solves the problem of
convolutional kernel parameter sharing. However, the information contained in
the attention map generated by spatial attention is not sufficient for
large-size convolutional kernels. Therefore, we propose a novel attention
mechanism called Receptive-Field Attention (RFA). Existing spatial attention,
such as Convolutional Block Attention Module (CBAM) and Coordinated Attention
(CA) focus only on spatial features, which does not fully address the problem
of convolutional kernel parameter sharing. In contrast, RFA not only focuses on
the receptive-field spatial feature but also provides effective attention
weights for large-size convolutional kernels. The Receptive-Field Attention
convolutional operation (RFAConv), developed by RFA, represents a new approach
to replace the standard convolution operation. It offers nearly negligible
increment of computational cost and parameters, while significantly improving
network performance. We conducted a series of experiments on ImageNet-1k, COCO,
and VOC datasets to demonstrate the superiority of our approach. Of particular
importance, we believe that it is time to shift focus from spatial features to
receptive-field spatial features for current spatial attention mechanisms. In
this way, we can further improve network performance and achieve even better
results. The code and pre-trained models for the relevant tasks can be found at
https://github.com/Liuchen1997/RFAConv.
