MS-Former：基于记忆支持的Transformer用于补丁级标注的弱监督变化检测
MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations
AI总结：
提出MS-Former框架，通过双向注意力块和补丁级监督方案，利用补丁级标注进行弱监督变化检测，在三个基准数据集上验证了有效性。
摘要：
全监督变化检测方法在性能上取得了显著进展，但它们严重依赖于获取昂贵的像素级标签。考虑到补丁级标注也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补丁级标注来分割变化区域。如何从补丁级标注中捕获与变化和未变化区域相关的语义变化，以获得有前景的变化检测结果，是弱监督变化检测任务中的关键挑战。在本文中，我们提出了一种记忆支持的Transformer（MS-Former），这是一个新颖的框架，包含双向注意力块（BAB）和补丁级监督方案（PSS），专门针对具有补丁级标注的弱监督变化检测而设计。具体来说，BAM从时间差异特征中捕获与变化和未变化区域相关的上下文，以构建存储在记忆库中的信息性原型。另一方面，BAM从原型中提取有用信息作为补充上下文，以增强时间差异特征，从而更好地区分变化和未变化区域。之后，PSS引导网络从补丁级标注中学习有价值的知识，从而进一步提升性能。在三个基准数据集上的实验结果表明，我们提出的方法在变化检测任务中的有效性。我们工作的演示代码将在https://github.com/guanyuezhen/MS-Former上公开提供。
英文摘要：
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}.
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。
