  Change detection (CD) aims to identify changes that occur in an image pair
taken different times. Prior methods devise specific networks from scratch to
predict change masks in pixel-level, and struggle with general segmentation
problems. In this paper, we propose a new paradigm that reduces CD to semantic
segmentation which means tailoring an existing and powerful semantic
segmentation network to solve CD. This new paradigm conveniently enjoys the
mainstream semantic segmentation techniques to deal with general segmentation
problems in CD. Hence we can concentrate on studying how to detect changes. We
propose a novel and importance insight that different change types exist in CD
and they should be learned separately. Based on it, we devise a module named
MTF to extract the change information and fuse temporal features. MTF enjoys
high interpretability and reveals the essential characteristic of CD. And most
segmentation networks can be adapted to solve the CD problems with our MTF
module. Finally, we propose C-3PO, a network to detect changes at pixel-level.
C-3PO achieves state-of-the-art performance without bells and whistles. It is
simple but effective and can be considered as a new baseline in this field. Our
code is at https://github.com/DoctorKey/C-3PO.
