Semi-Supervised Object Detection (SSOD) has achieved resounding success by
leveraging unlabeled data to improve detection performance. However, in Open
Scene Semi-Supervised Object Detection (O-SSOD), unlabeled data may contains
unknown objects not observed in the labeled data, which will increase
uncertainty in the model's predictions for known objects. It is detrimental to
the current methods that mainly rely on self-training, as more uncertainty
leads to the lower localization and classification precision of pseudo labels.
To this end, we propose Credible Teacher, an end-to-end framework. Credible
Teacher adopts an interactive teaching mechanism using flexible labels to
prevent uncertain pseudo labels from misleading the model and gradually reduces
its uncertainty through the guidance of other credible pseudo labels. Empirical
results have demonstrated our method effectively restrains the adverse effect
caused by O-SSOD and significantly outperforms existing counterparts.

通过采用可信教师模型，本研究提出了一种端到端框架，使用灵活的标签来防止不确定的伪标签对模型的误导，逐步减少其不确定性，并通过其他可信伪标签的指导有效地抑制 O-SSOD 引起的负面影响，并且在实证结果中显著优于现有的对应模型。