通过对抗性类别提示推进卫星图像弱监督变化检测
Advancing Weakly-Supervised Change Detection in Satellite Images via Adversarial Class Prompting
AI总结：
提出对抗性类别提示方法，通过对抗性扰动和原型校正提升卫星图像弱监督变化检测性能。
摘要：
弱监督变化检测（WSCD）旨在区分特定物体变化（例如，物体出现或消失）与背景变化（例如，由于光照、天气或季节变化引起的环境变化）在成对的卫星图像中，仅依赖于成对图像（即图像级）分类标签。这种技术显著减少了全监督变化检测中所需密集标注的数量。然而，由于图像级监督仅表明场景中物体是否发生变化，WSCD方法往往将背景变化误判为物体变化，尤其是在复杂的遥感场景中。在本工作中，我们提出了一种对抗性类别提示（AdvCP）方法来解决这种共现噪声问题，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，使用错误的one-hot图像级标签来激活错误的特征映射。此过程揭示了在弱监督下共现的对抗样本，即可能被误判为物体变化的背景变化特征。b) 对抗样本校正：我们将这些由对抗性提示激活的像素样本整合到训练中，通过构建一个在线全局原型。此原型由当前批次和所有历史训练数据的指数加权移动平均值构建。我们的AdvCP可以无缝集成到当前WSCD方法中，而无需增加额外的推理成本。在ConvNet、Transformer和Segment Anything Model（SAM）基线上的实验显示了显著的性能提升。此外，我们展示了AdvCP在其他多类弱监督密集预测场景中的通用性。代码可在https://github.com/zhenghuizhao/AdvCP上获得。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
