Multi-scale global context feature pyramid network for object detector

In order to capture more contextual information, various attention mechanisms are applied to object detectors. However, the spatial interaction in the commonly used attention mechanisms is single scale, and it cannot capture the context information of the objects from the feature maps of different scales, which will lead to the underutilization of the context information. In addition, since the predicted bounding box does not completely fit the shape and pose of the object, it has room for further improvement in the performance. In this paper, we propose a multi-scale global context feature pyramid network to obtain a feature pyramid with richer context information, which is a two-layer lightweight neck structure. Moreover, we extend the regression branch by adding an additional prediction head to predict the corner offsets of the bounding boxes to further refine the bounding boxes, which can effectively improve the accuracy of the predicted bounding boxes. Extensive experiments are conducted on the MS COCO 2017 detection datasets. Without bells and whistles, the proposed methods show an average 2% improvement over the RetinaNet baseline.