  Classical CNN based object detection methods only extract the objects' image
features, but do not consider the high-level relationship among objects in
context. In this article, the graph convolutional networks (GCN) is integrated
into the object detection framework to exploit the benefit of category
relationship among objects, which is able to provide extra confidence for any
pre-trained object detection model in our framework. In experiments, we test
several popular base detection models on COCO dataset. The results show
promising improvement on mAP by 1-5pp. In addition, visualized analysis reveals
the benchmark improvement is quite reasonable in human's opinion.
