Segment Any Change
arxiv(2024)
摘要
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
score, and achieving comparable accuracy with negligible manual annotations (1
pixel per image) for supervised change detection.
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