  Object detection has been a challenging task in computer vision. Although
significant progress has been made in object detection with deep neural
networks, the attention mechanism is far from development. In this paper, we
propose the hybrid attention mechanism for single-stage object detection.
First, we present the modules of spatial attention, channel attention and
aligned attention for single-stage object detection. In particular, stacked
dilated convolution layers with symmetrically fixed rates are constructed to
learn spatial attention. The channel attention is proposed with the cross-level
group normalization and squeeze-and-excitation module. Aligned attention is
constructed with organized deformable filters. Second, the three kinds of
attention are unified to construct the hybrid attention mechanism. We then
embed the hybrid attention into Retina-Net and propose the efficient
single-stage HAR-Net for object detection. The attention modules and the
proposed HAR-Net are evaluated on the COCO detection dataset. Experiments
demonstrate that hybrid attention can significantly improve the detection
accuracy and the HAR-Net can achieve the state-of-the-art 45.8\% mAP,
outperform existing single-stage object detectors.
