Dual-Teacher Prototype Consistency for Unsupervised Domain Adaptive Object Detection in Remote Sensing Images (opens in new tab)

Unsupervised domain-adaptive object detection (UDA-OD) in remote sensing images aims to transfer knowledge from a labeled source domain to an unlabeled target domain, mitigating performance degradation caused by domain shift. Existing methods commonly adopt a teacher–student framework, where a single teacher, updated via an exponential moving average (EMA) of the student parameters, generates pseudolabels for training. While this framework effectively transfers knowledge from the teacher to t...