OD-GCN: Object Detection Boosted by Knowledge GCN
This work addresses the limitation of classical CNN-based object detection methods by incorporating contextual object relationships, offering an incremental enhancement for computer vision applications.
The paper tackles the problem of object detection by integrating graph convolutional networks (GCN) to model high-level relationships among objects, boosting pre-trained models with extra confidence and achieving improvements of 1-5 percentage points in mAP on the COCO dataset.
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.