LAUSR

The core task of object detection is to extract features of various sizes by hierarchically stacking multi-scale feature maps. However, it is not easy to decide whether we should transmit semantic information to the low layers while reducing the loss of semantic information of the high-level features. In this paper, we present a novel method to reduce the loss of semantic information, and at the same time to improve the object detection performance by using the attention mechanism on the high-level layer of the feature pyramid network. The proposed method focuses on the sparse spatial information using deformable convolution v2 (DCNv2) on the lateral connection in the feature pyramid network. Specifically, the upsampling process is divided into two branches. The first one pays attention to the global context information of high-level features, and the other rescales the feature map by interpolation. Finally, by multiplying the results from the two branches, we can obtain upsampling result that pays attention to semantic information of the high-level layer. The proposed pyramid attention upsampling module has three contributions. First, It can be easily applied to any models using feature pyramid network. Second, it is possible to reduce losses in semantic information of the high-level feature map by performing context attention of the high-level layer. Third, it improves the detection performance by stacking layers up to the low layer. We used MS-COCO 2017 detection dataset to evaluate the performance of the proposed method. Experimental results show that the proposed method provided better detection performance comparing with existing feature pyramid network-base methods.