Object Correlation Matrix for Two-Stage Object Detection Network

The relationship between various objects in real life is very important and universal. However, existing object detection models, especially Two-stage models, mostly rely solely on instance learning of individual objects, which use limited global information to extract regions of interest and neglect the correlation between object instances. Therefore, this article proposes an Object Correlation Matrix (OCM) for measuring the correlation between objects, obtained by statistical analysis of instance information in the MS COCO dataset. Based on OCM, we design a Secondary Scoring Module (SSM) that combines the confidence and correlation of surrounding objects to correct the predicted outputs of the original network. Then apply the SSM to the post-processing stage, which is entirely independent of the original network and does not require adding any parameters or retraining the original network. Hence, this module is easy to embed in object detection networks and has achieved performance improvement.