  Object detection often costs a considerable amount of computation to get
satisfied performance, which is unfriendly to be deployed in edge devices. To
address the trade-off between computational cost and detection accuracy, this
paper presents a dual path network, named DPNet, for efficient object detection
with lightweight self-attention. In backbone, a single input/output lightweight
self-attention module (LSAM) is designed to encode global interactions between
different positions. LSAM is also extended into a multiple-inputs version in
feature pyramid network (FPN), which is employed to capture cross-resolution
dependencies in two paths. Extensive experiments on the COCO dataset
demonstrate that our method achieves state-of-the-art detection results. More
specifically, DPNet obtains 29.0% AP on COCO test-dev, with only 1.14 GFLOPs
and 2.27M model size for a 320x320 image.
