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.

翻译：变化检测（CD）旨在识别不同时间拍摄的图像对中发生的变化。以往的方法从头设计特定的网络来逐像素预测变化掩膜，并难以应对通用的分割问题。本文提出了一种新范式，将变化检测简化为语义分割，即通过改造现有的强大语义分割网络来解决CD问题。该新范式可便捷地利用主流语义分割技术处理CD中的通用分割问题，从而使我们能够专注于研究如何检测变化。我们提出了一项新颖且重要的见解：CD中存在不同类型的变化，这些变化应被分别学习。基于此，我们设计了一个名为MTF的模块，用于提取变化信息并融合时间特征。MTF具有高可解释性，揭示了CD的本质特性。利用我们的MTF模块，大多数分割网络均可适应解决CD问题。最后，我们提出了C-3PO网络，用于逐像素检测变化。C-3PO无需繁琐技巧即实现了当前最优性能，简洁而高效，可被视为该领域的新基线。我们的代码位于https://github.com/DoctorKey/C-3PO。