Change Detection (CD) aims to identify pixels with semantic changes between images. However, annotating massive numbers of pixel-level images is labor-intensive and costly, especially for multi-temporal images, which require pixel-wise comparisons by human experts. Considering the excellent performance of visual language models (VLMs) for zero-shot, open-vocabulary, etc. with prompt-based reasoning, it is promising to utilize VLMs to make better CD under limited labeled data. In this paper, we propose a VLM guidance-based semi-supervised CD method, namely DiffMatch. The insight of DiffMatch is to synthesize free change labels using VLMs to provide additional supervision signals for unlabeled data. However, almost all current VLMs are designed for single-temporal images and cannot be directly applied to bi- or multi-temporal images. Motivated by this, we first propose a VLM-based mixed change event generation (CEG) strategy to yield pseudo labels for unlabeled CD data. Since the additional supervised signals provided by these VLM-driven pseudo labels may conflict with the pseudo labels from the consistency regularization paradigm (e.g. FixMatch), we propose the dual projection head for de-entangling different signal sources. Further, we explicitly decouple the bi-temporal images semantic representation through two auxiliary segmentation decoders, which are also guided by VLM. Finally, to make the model more adequately capture change representations, we introduce metric-aware supervision by feature-level contrastive loss in auxiliary branches. Extensive experiments show the advantage of DiffMatch. For instance, DiffMatch improves the FixMatch baseline by +5.3 IoU on WHU-CD and by +2.4 IoU on LEVIR-CD with 5% labels. In addition, our CEG strategy, in an un-supervised manner, can achieve performance far superior to state-of-the-art un-supervised CD methods.

翻译：变化检测旨在识别图像间发生语义变化的像素。然而，注释大量像素级图像是劳动密集且成本高昂的，尤其是对于需要人类专家逐像素比较的多时相图像。考虑到视觉语言模型（VLM）在零样本、开放词汇等场景中基于提示推理的卓越性能，利用VLM在有限标注数据下改进变化检测具有前景。本文提出一种基于VLM引导的半监督变化检测方法，即DiffMatch。其核心思想是利用VLM合成免费的变化标签，为未标注数据提供额外监督信号。然而，当前几乎所有VLM均针对单时相图像设计，无法直接应用于双时相或多时相图像。受此启发，我们首先提出基于VLM的混合变化事件生成策略，为未标注变化检测数据生成伪标签。由于这些VLM驱动的伪标签提供的额外监督信号可能与一致性正则化范式（如FixMatch）生成的伪标签产生冲突，我们提出双投影头以解耦不同信号源。进一步，我们通过两个辅助分割解码器显式解耦双时相图像的语义表示，这些解码器同样受VLM引导。最终，为使模型更充分捕获变化表示，我们在辅助分支中引入基于特征级对比损失的度量感知监督。大量实验证明了DiffMatch的优势。例如，在WHU-CD数据集上使用5%标签时，DiffMatch将FixMatch基线提升+5.3 IoU，在LEVIR-CD上提升+2.4 IoU。此外，我们的CEG策略以无监督方式即可实现远超现有最优无监督变化检测方法的性能。