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Change detection (CD) is a critical task to observe and analyze dynamicprocesses of land cover. Although numerous deep learning-based CD models haveperformed excellently, their further performance improvements are constrainedby the limited knowledge extracted from the given labelled data. On the otherhand, the foundation models that emerged recently contain a huge amount ofknowledge by scaling up across data modalities and proxy tasks. In this paper,we propose a Bi-Temporal Adapter Network (BAN), which is a universal foundationmodel-based CD adaptation framework aiming to extract the knowledge offoundation models for CD. The proposed BAN contains three parts, i.e. frozenfoundation model (e.g., CLIP), bitemporal adapter branch (Bi-TAB), and bridgingmodules between them. Specifically, the Bi-TAB can be either an existingarbitrary CD model or some hand-crafted stacked blocks. The bridging modulesare designed to align the general features with the task/domain-specificfeatures and inject the selected general knowledge into the Bi-TAB. To ourknowledge, this is the first universal framework to adapt the foundation modelto the CD task. Extensive experiments show the effectiveness of our BAN inimproving the performance of existing CD methods (e.g., up to 4.08\% IoUimprovement) with only a few additional learnable parameters. More importantly,these successful practices show us the potential of foundation models forremote sensing CD. The code is available at \url{https://github.com/likyoo/BAN}and will be supported in our Open-CD \url{https://github.com/likyoo/open-cd}.

A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection