I present the Lower Biased Teacher model, an enhancement of the Unbiased Teacher model, specifically tailored for semi-supervised object detection tasks. The primary innovation of this model is the integration of a localization loss into the teacher model, which significantly improves the accuracy of pseudo-label generation. By addressing key issues such as class imbalance and the precision of bounding boxes, the Lower Biased Teacher model demonstrates superior performance in object detection tasks. Extensive experiments on multiple semi-supervised object detection datasets show that the Lower Biased Teacher model not only reduces the pseudo-labeling bias caused by class imbalances but also mitigates errors arising from incorrect bounding boxes. As a result, the model achieves higher mAP scores and more reliable detection outcomes compared to existing methods. This research underscores the importance of accurate pseudo-label generation and provides a robust framework for future advancements in semi-supervised learning for object detection.

翻译：本文提出下偏置教师模型，该模型是对无偏教师模型的改进，专门针对半监督目标检测任务而设计。该模型的主要创新在于将定位损失集成到教师模型中，从而显著提升了伪标签生成的准确性。通过解决类别不平衡和边界框精度等关键问题，下偏置教师模型在目标检测任务中展现出卓越性能。在多个半监督目标检测数据集上的大量实验表明，下偏置教师模型不仅减少了由类别不平衡引起的伪标签偏置，还缓解了因错误边界框而产生的误差。因此，与现有方法相比，该模型获得了更高的mAP分数和更可靠的检测结果。本研究强调了准确生成伪标签的重要性，并为未来半监督目标检测学习的进一步发展提供了一个稳健的框架。