Weakly-Supervised Change Detection (WSCD) aims to distinguish specific object changes (e.g., objects appearing or disappearing) from background variations (e.g., environmental changes due to light, weather, or seasonal shifts) in paired satellite images, relying only on paired image (i.e., image-level) classification labels. This technique significantly reduces the need for dense annotations required in fully-supervised change detection. However, as image-level supervision only indicates whether objects have changed in a scene, WSCD methods often misclassify background variations as object changes, especially in complex remote-sensing scenarios. In this work, we propose an Adversarial Class Prompting (AdvCP) method to address this co-occurring noise problem, including two phases: a) Adversarial Prompt Mining: After each training iteration, we introduce adversarial prompting perturbations, using incorrect one-hot image-level labels to activate erroneous feature mappings. This process reveals co-occurring adversarial samples under weak supervision, namely background variation features that are likely to be misclassified as object changes. b) Adversarial Sample Rectification: We integrate these adversarially prompt-activated pixel samples into training by constructing an online global prototype. This prototype is built from an exponentially weighted moving average of the current batch and all historical training data. Our AdvCP can be seamlessly integrated into current WSCD methods without adding additional inference cost. Experiments on ConvNet, Transformer, and Segment Anything Model (SAM)-based baselines demonstrate significant performance enhancements. Furthermore, we demonstrate the generalizability of AdvCP to other multi-class weakly-supervised dense prediction scenarios. Code is available at https://github.com/zhenghuizhao/AdvCP

翻译：弱监督变化检测（WSCD）旨在仅依赖成对图像（即图像级别）的分类标签，从成对的卫星图像中区分特定物体变化（如物体出现或消失）与背景变化（如光照、天气或季节变化引起的环境变化）。该技术显著减少了全监督变化检测所需的密集标注需求。然而，由于图像级监督仅指示场景中物体是否发生变化，WSCD方法常将背景变化误分类为物体变化，尤其在复杂的遥感场景中。本研究提出一种对抗性类别提示（AdvCP）方法以解决此共现噪声问题，包括两个阶段：a) 对抗性提示挖掘：在每次训练迭代后，我们引入对抗性提示扰动，利用错误的独热编码图像级标签激活错误特征映射。此过程揭示了弱监督下共现的对抗样本，即可能被误分类为物体变化的背景变化特征。b) 对抗样本校正：我们通过构建在线全局原型，将这些对抗性提示激活的像素样本整合到训练中。该原型基于当前批次与所有历史训练数据的指数加权移动平均构建。我们的AdvCP方法可无缝集成到现有WSCD方法中，且不增加额外推理成本。在基于ConvNet、Transformer和Segment Anything Model（SAM）的基准模型上的实验表明性能显著提升。此外，我们验证了AdvCP在其他多类别弱监督密集预测场景中的泛化能力。代码发布于https://github.com/zhenghuizhao/AdvCP