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insight - Semi-Supervised Object Detection
Boosting Semi-Supervised Object Detection in Remote Sensing Images with Active Teaching
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.
Online Open-set Semi-supervised Object Detection with Dual Competing Head
Proposing an online end-to-end OSSOD framework with semi-supervised outlier filtering and a Dual Competing OOD head to improve performance.
Enhancing Semi-Supervised Object Detection with the Lower Biased Teacher Model
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.
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