HAR-Net: Joint Learning of Hybrid Attention for Single-Stage Object Detection

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 has yet to be fully developed. In this paper, we propose a 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, dilated convolution layers with symmetrically fixed rates are stacked to learn spatial attention. A channel attention mechanism with the cross-level group normalization and squeeze-and-excitation operation is proposed. Aligned attention is constructed with organized deformable filters. Second, the three types of attention are unified to construct the hybrid attention mechanism. We then plug 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. The experiments demonstrate that hybrid attention can significantly improve the detection accuracy and that the HAR-Net can achieve a state-of-the-art 45.8% mAP, thus outperforming existing single-stage object detectors.