  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 SemiCD-VL. The insight of SemiCD-VL 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 SemiCD-VL. For instance, SemiCD-VL 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.
