Background-Mixed Augmentation for Weakly Supervised Change Detection


  • Rui Huang Civil Aviation University of China
  • Ruofei Wang Civil Aviation University of China
  • Qing Guo Center for Frontier AI Research (CFAR), A*STAR, Singapore
  • Jieda Wei Civil Aviation University of China
  • Yuxiang Zhang Civil Aviation University of China
  • Wei Fan Civil Aviation University of China
  • Yang Liu Zhejiang Sci-Tech University, China Nanyang Technology University, Singapore



ML: Semi-Supervised Learning, CV: Scene Analysis & Understanding


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




How to Cite

Huang, R., Wang, R., Guo, Q., Wei, J., Zhang, Y., Fan, W., & Liu, Y. (2023). Background-Mixed Augmentation for Weakly Supervised Change Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 37(7), 7919-7927.



AAAI Technical Track on Machine Learning II