Segment Any Change
<|reference_start|>Segment Any Change: 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.<|reference_end|>
@article{zheng2024segment,
    title={Segment Any Change},
    author={Zhuo Zheng, Yanfei Zhong, Liangpei Zhang, Stefano Ermon},
    journal={arXiv preprint arXiv:2402.01188},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01188},
    primaryClass={cs.CV}
}