  Recent studies on mobile network design have demonstrated the remarkable
effectiveness of channel attention (e.g., the Squeeze-and-Excitation attention)
for lifting model performance, but they generally neglect the positional
information, which is important for generating spatially selective attention
maps. In this paper, we propose a novel attention mechanism for mobile networks
by embedding positional information into channel attention, which we call
"coordinate attention". Unlike channel attention that transforms a feature
tensor to a single feature vector via 2D global pooling, the coordinate
attention factorizes channel attention into two 1D feature encoding processes
that aggregate features along the two spatial directions, respectively. In this
way, long-range dependencies can be captured along one spatial direction and
meanwhile precise positional information can be preserved along the other
spatial direction. The resulting feature maps are then encoded separately into
a pair of direction-aware and position-sensitive attention maps that can be
complementarily applied to the input feature map to augment the representations
of the objects of interest. Our coordinate attention is simple and can be
flexibly plugged into classic mobile networks, such as MobileNetV2, MobileNeXt,
and EfficientNet with nearly no computational overhead. Extensive experiments
demonstrate that our coordinate attention is not only beneficial to ImageNet
classification but more interestingly, behaves better in down-stream tasks,
such as object detection and semantic segmentation. Code is available at
https://github.com/Andrew-Qibin/CoordAttention.
