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.
@article{arxiv.1908.04385,
title = {OD-GCN: Object Detection Boosted by Knowledge GCN},
author = {Zheng Liu and Zidong Jiang and Wei Feng and Hui Feng},
journal= {arXiv preprint arXiv:1908.04385},
year = {2019}
}