Feature Transform Correlation Network for Object Detection

Abstract

With the development of computer vision and the growth of image data volume, object detection is more and more widely used in people's life. Recently, object detection network base on transformer has good effect. However, it doesn't pay attention to objects' distinguishable features and the correlation of different distinguishable features, which makes its accuracy not satisfactory. In this paper, we propose a new transform correlation network (FTCN) based on rotational attention for object detection, which can be inserted into various modules of the object detection network theoretically. It can help the network to pay more attention to distinguishable features and their correlation, and increase the accuracy of the network. Compared to transformer, FTCN reduces the large computational cost and parameters that encoders and decoders require. Experimental results show that FTCN can increase the accuracy of the network to a certain extent in Microsoft COCO and Pascal VOC. The comparative results of GFLOPs and Param show that our approach has less computational cost and parameters compared with transformer methods.