Visual foundation models have achieved remarkable results in zero-shot image classification and segmentation, but zero-shot change detection remains an open problem. In this paper, we propose the segment any change models (AnyChange), a new type of change detection model that supports zero-shot prediction and generalization on unseen change types and data distributions. AnyChange is built on the segment anything model (SAM) via our training-free adaptation method, bitemporal latent matching. By revealing and exploiting intra-image and inter-image semantic similarities in SAM's latent space, bitemporal latent matching endows SAM with zero-shot change detection capabilities in a training-free way. We also propose a point query mechanism to enable AnyChange's zero-shot object-centric change detection capability. We perform extensive experiments to confirm the effectiveness of AnyChange for zero-shot change detection. AnyChange sets a new record on the SECOND benchmark for unsupervised change detection, exceeding the previous SOTA by up to 4.4% F$_1$ score, and achieving comparable accuracy with negligible manual annotations (1 pixel per image) for supervised change detection.

在这篇文章中，我们提出了一种新的变化检测模型，称为Segment Any Change Models（AnyChange），它通过训练无关的自适应方法，在零样本预测和泛化的情况下支持不同类型和数据分布的变化检测。AnyChange通过在Segment Anything Model（SAM）的潜在空间中揭示和利用图像内部及图像间的语义相似性来实现零样本变化检测能力。我们还提出了一种点查询机制，使得AnyChange具备了零样本目标中心变化检测的能力。通过大量实验证明了AnyChange在零样本变化检测方面的有效性。AnyChange在SECOND基准测试中取得了令人瞩目的成绩，F$_1$分数超过了之前的最佳结果4.4％，并在有限的手动注释（每个图像1个像素）的监督变化检测上实现了可比较的准确性。

分割任何变化