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.
A survey of convolutional neural networks: analysis, applications, and prospects.IEEE transactions on neural networks and learning systems, 33(12):6999–7019
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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.