UniCD unifies supervised, weakly-supervised, and unsupervised change detection via a shared encoder and collaborative branches, claiming large accuracy gains in low-supervision settings on datasets like LEVIR-CD.
Da 2-net: Integrating sam2 with domain adaption and dif- ference aggregation for remote sensing change detection
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Bridging Supervision Gaps: A Unified Framework for Remote Sensing Change Detection
UniCD unifies supervised, weakly-supervised, and unsupervised change detection via a shared encoder and collaborative branches, claiming large accuracy gains in low-supervision settings on datasets like LEVIR-CD.