Semi-supervised object detection (SSOD) can incorporate limited labeled data and large amounts of unlabeled data to improve the performance and generalization of existing object detectors. Despite many advances, recent SSOD methods are still challenged by noisy/misleading pseudo-labels, classical exponential moving average (EMA) strategy, and the consensus of Teacher-Student models in the latter stages of training. This paper proposes a novel training-based model refinement (TMR) stage and a simple yet effective representation disagreement (RD) strategy to address the limitations of classical EMA and the consensus problem. The TMR stage of Teacher-Student models optimizes the lightweight scaling operation to refine the model's weights and prevent overfitting or forgetting learned patterns from unlabeled data. Meanwhile, the RD strategy helps keep these models diverged to encourage the student model to explore complementary representations. In addition, we use cascade regression to generate more reliable pseudo-labels for supervising the student model. Extensive experiments demonstrate the superior performance of our approach over state-of-the-art SSOD methods. Specifically, the proposed approach outperforms the Unbiased-Teacher method by an average mAP margin of 4.6% and 5.3% when using partially-labeled and fully-labeled data on the MS-COCO dataset, respectively.

翻译：半监督目标检测（SSOD）可通过整合少量标注数据与大量未标注数据，提升现有目标检测器的性能与泛化能力。尽管取得诸多进展，当前SSOD方法仍面临噪声/误导性伪标签、经典指数移动平均（EMA）策略以及训练后期师生模型趋同等挑战。本文提出一种新型基于训练模型优化（TMR）阶段与简洁有效的表示差异（RD）策略，以解决经典EMA方法的局限性及趋同问题。师生模型的TMR阶段通过优化轻量级缩放操作来精调模型权重，防止因未标注数据导致的过拟合或模式遗忘。同时，RD策略有助于保持模型间差异，推动学生模型探索互补表示。此外，我们采用级联回归生成更可靠的伪标签以监督学生模型。大量实验表明，本方法在性能上优于现有最先进的SSOD方法。具体而言，在MS-COCO数据集上使用部分标注数据与全标注数据时，本方法相较Unbiased-Teacher方法平均mAP分别提升4.6%和5.3%。