Segment Any Change

• URL: http://arxiv.org/abs/2402.01188v3
• Date: Fri, 27 Sep 2024 01:13:29 GMT
• Title: Segment Any Change
• Authors: Zhuo Zheng, Yanfei Zhong, Liangpei Zhang, Stefano Ermon,
• Abstract summary: We propose 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. We also propose a point query mechanism to enable AnyChange's zero-shot object-centric change detection capability.
• Score: 64.23961453159454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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