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.

本文提出了一种基于视觉语言模型的半监督变化检测方法，名为 DiffMatch，通过利用 VLM 合成自由变化标签为无标签数据提供额外的监督信号，并通过辅助分割解码器明确分离双时相图像的语义表示，最终通过特征级对比损失引入度量感知监督，实验证明 DiffMatch 相较于基线方法 FixMatch 在变化检测上具有明显优势。