Cross-View Change Detection via Self-Supervised Learning
A self-supervised contrastive and predictive learning framework that learns discriminative representations directly from unlabeled cross-view image pairs is proposed, which jointly exploits patch-level and pixel-level objectives to capture both fine-grained local changes and higher-level semantic consistency across vie...