Fusion of Different Features by Cross Cooperative Learning for Semantic Segmentation

Abstract

Deep neural networks have achieved high accuracy in the field of image recognition. Its technology is expected to use the medical, autonomous driving and so on. Therefore, various deep learning methods have been studied for many years. Recently, many studies used a backbone network as an encoder for feature extraction. Of course, the extracted features are changed when we change backbone networks. This paper focused on the differences in features extracted from two backbone networks. It will be possible to obtain the information that cannot be obtained by a single backbone network, and we can get rich information to solve a task. In addition, we use cross cooperative learning for fusing the features of different backbone networks effectively. In experiments on two kinds of datasets for image segmentation, our proposed method achieved better segmentation accuracy than conventional method using a single backbone network and the ensemble