  Change detection (CD) is to decouple object changes (i.e., object missing or
appearing) from background changes (i.e., environment variations) like light
and season variations in two images captured in the same scene over a long time
span, presenting critical applications in disaster management, urban
development, etc. In particular, the endless patterns of background changes
require detectors to have a high generalization against unseen environment
variations, making this task significantly challenging. Recent deep
learning-based methods develop novel network architectures or optimization
strategies with paired-training examples, which do not handle the
generalization issue explicitly and require huge manual pixel-level annotation
efforts. In this work, for the first attempt in the CD community, we study the
generalization issue of CD from the perspective of data augmentation and
develop a novel weakly supervised training algorithm that only needs
image-level labels. Different from general augmentation techniques for
classification, we propose the background-mixed augmentation that is
specifically designed for change detection by augmenting examples under the
guidance of a set of background-changing images and letting deep CD models see
diverse environment variations. Moreover, we propose the augmented & real data
consistency loss that encourages the generalization increase significantly. Our
method as a general framework can enhance a wide range of existing deep
learning-based detectors. We conduct extensive experiments in two public
datasets and enhance four state-of-the-art methods, demonstrating the
advantages of our method. We release the code at
https://github.com/tsingqguo/bgmix.
