The author proposes a novel active learning method, SSOD-AT, to enhance semi-supervised object detection in remote sensing images using a teacher-student network. By incorporating a RoI Comparison module and diversity criterion, the method outperforms state-of-the-art techniques.
Proposing an online end-to-end OSSOD framework with semi-supervised outlier filtering and a Dual Competing OOD head to improve performance.
The Lower Biased Teacher model improves the accuracy of pseudo-label generation in semi-supervised object detection tasks by integrating a localization loss into the teacher model, addressing key issues such as class imbalance and bounding box precision.