In recent years, change detection (CD) in remote sensing images has achieved a huge success by using deep learning, and it is essentially a subtask of semantic segmentation. Both of them are tightly related. However, the complementarity of them has not been fully explored. In this paper, we propose a new change detection converter (CDC), which can be easily inserted into the existing semantic segmentation networks and be transformed into new networks suitable for change detection task. In addition, a new task-specific data augmentation method named Crossover is proposed, which can enhance the feature representation ability in sequence-form features without any additional cost. Extensive experiments demonstrate that the proposed method obtains significantly better performance and more efficiency than previous methods on two public datasets, and can achieve continuous performance improvement than other complex-designed solutions.