Fully supervised change detection methods have achieved significant advancements in performance, yet they depend severely on acquiring costly pixel-level labels. Considering that the patch-level annotations also contain abundant information corresponding to both changed and unchanged objects in bi-temporal images, an intuitive solution is to segment the changes with patch-level annotations. How to capture the semantic variations associated with the changed and unchanged regions from the patch-level annotations to obtain promising change results is the critical challenge for the weakly supervised change detection task. In this paper, we propose a memory-supported transformer (MS-Former), a novel framework consisting of a bi-directional attention block (BAB) and a patch-level supervision scheme (PSS) tailored for weakly supervised change detection with patch-level annotations. More specifically, the BAM captures contexts associated with the changed and unchanged regions from the temporal difference features to construct informative prototypes stored in the memory bank. On the other hand, the BAM extracts useful information from the prototypes as supplementary contexts to enhance the temporal difference features, thereby better distinguishing changed and unchanged regions. After that, the PSS guides the network learning valuable knowledge from the patch-level annotations, thus further elevating the performance. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method in the change detection task. The demo code for our work will be publicly available at \url{https://github.com/guanyuezhen/MS-Former}.

翻译：全监督变化检测方法在性能上取得了显著进展，但其严重依赖获取昂贵像素级标注。考虑到图像块级标注同样包含与双时相图像中变化和未变化对象相关的丰富信息，一个直观的解决方案是利用图像块级标注进行变化分割。如何从图像块级标注中捕获与变化和未变化区域相关的语义变化，从而获得理想的变化检测结果，是弱监督变化检测任务面临的关键挑战。本文提出一种基于记忆增强的Transformer（MS-Former），该框架包含双向注意力模块（BAB）和针对图像块级标注弱监督变化检测设计的图像块级监督方案（PSS）。具体而言，BAM从时序差异特征中捕获与变化和未变化区域相关的上下文信息，构建存储在记忆库中的信息原型；同时，BAM从原型中提取有效信息作为补充上下文，增强时序差异特征，从而更好地区分变化与未变化区域。随后，PSS引导网络从图像块级标注中学习有价值的知识，进一步提升性能。在三个基准数据集上的实验结果表明，所提方法在变化检测任务中具有有效性。本工作的演示代码将在\url{https://github.com/guanyuezhen/MS-Former}公开提供。