Semi-supervised object detection (SSOD) aims to facilitate the training and
deployment of object detectors with the help of a large amount of unlabeled
data. Though various self-training based and consistency-regularization based
SSOD methods have been proposed, most of them are anchor-based detectors,
ignoring the fact that in many real-world applications anchor-free detectors
are more demanded. In this paper, we intend to bridge this gap and propose a
DenSe Learning (DSL) based anchor-free SSOD algorithm. Specifically, we achieve
this goal by introducing several novel techniques, including an Adaptive
Filtering strategy for assigning multi-level and accurate dense pixel-wise
pseudo-labels, an Aggregated Teacher for producing stable and precise
pseudo-labels, and an uncertainty-consistency-regularization term among scales
and shuffled patches for improving the generalization capability of the
detector. Extensive experiments are conducted on MS-COCO and PASCAL-VOC, and
the results show that our proposed DSL method records new state-of-the-art SSOD
performance, surpassing existing methods by a large margin. Codes can be found
at \textcolor{blue}{this https URL}.

本研究提出了一种基于 DenSe Learning 的锚点自由的半监督目标检测算法，其中包含自适应过滤策略、聚合型教师以及跨尺度不确定性一致性正则化等多种新技术，与 MS-COCO 和 PASCAL-VOC 等数据集上得到的实验结果表明，该方法在半监督目标检测性能上取得了新的最优结果。