Weakly-supervised change detection (WSCD) aims to detect pixel-level changes with only image-level annotations. Owing to its label efficiency, WSCD is drawing increasing attention recently. However, current WSCD methods often encounter the challenge of change missing and fabricating, i.e., the inconsistency between image-level annotations and pixel-level predictions. Specifically, change missing refer to the situation that the WSCD model fails to predict any changed pixels, even though the image-level label indicates changed, and vice versa for change fabricating. To address this challenge, in this work, we leverage global-scale and local-scale priors in WSCD and propose two components: a Dilated Prior (DP) decoder and a Label Gated (LG) constraint. The DP decoder decodes samples with the changed image-level label, skips samples with the unchanged label, and replaces them with an all-unchanged pixel-level label. The LG constraint is derived from the correspondence between changed representations and image-level labels, penalizing the model when it mispredicts the change status. Additionally, we develop TransWCD, a simple yet powerful transformer-based model, showcasing the potential of weakly-supervised learning in change detection. By integrating the DP decoder and LG constraint into TransWCD, we form TransWCD-DL. Our proposed TransWCD and TransWCD-DL achieve significant +6.33% and +9.55% F1 score improvements over the state-of-the-art methods on the WHU-CD dataset, respectively. Some performance metrics even exceed several fully-supervised change detection (FSCD) competitors. Code will be available at https://github.com/zhenghuizhao/TransWCD.

翻译：弱监督变化检测（WSCD）旨在仅利用图像级标注检测像素级变化。由于其标注效率优势，WSCD近年来日益受到关注。然而，当前WSCD方法常面临变化遗漏与变化伪造的挑战，即图像级标注与像素级预测之间存在不一致性。具体而言，变化遗漏指WSCD模型未能预测任何变化像素，尽管图像级标签表明存在变化；变化伪造则反之。为解决这一挑战，本文利用WSCD中的全局尺度与局部尺度先验，提出两个组件：扩张先验（DP）解码器与标签门控（LG）约束。DP解码器对带有变化图像级标签的样本进行解码，跳过带有未变化标签的样本，并将其替换为全未变化的像素级标签。LG约束源于变化表示与图像级标签之间的对应关系，在模型误判变化状态时施加惩罚。此外，我们开发了TransWCD——一个简单而强大的基于Transformer的模型，展示了弱监督学习在变化检测中的潜力。通过将DP解码器与LG约束集成到TransWCD中，我们构建了TransWCD-DL。所提出的TransWCD与TransWCD-DL在WHU-CD数据集上分别较现有最优方法获得了+6.33%和+9.55%的F1分数提升，部分性能指标甚至超越了若干全监督变化检测（FSCD）方法。代码将发布于https://github.com/zhenghuizhao/TransWCD。