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实现像素级变化检测。由于其标注高效性，WSCD近年来受到广泛关注。然而，现有WSCD方法常面临变化遗漏与变化虚构的挑战，即图像级标注与像素级预测之间的不一致性。具体而言，变化遗漏是指模型在图像级标签指示存在变化时未能预测出任何变化像素，而变化虚构则相反。为应对这一挑战，本文利用WSCD中的全局尺度与局部尺度先验信息，提出两个组件：扩张先验（Dilated Prior, DP）解码器与标签门控（Label Gated, LG）约束。DP解码器对带有变化图像级标签的样本进行解码，跳过带有无变化标签的样本，并将其替换为全无变化的像素级标签。LG约束源于变化表征与图像级标签的对应关系，当模型误判变化状态时对其进行惩罚。此外，我们开发了TransWCD——一种简洁而强大的基于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约束集成至TransWCD，我们构建了TransWCD-DL。所提出的TransWCD与TransWCD-DL在WHU-CD数据集上分别比当前最优方法提升了6.33%与9.55%的F1分数。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。