We propose a semi-supervised approach for contemporary object detectors
following the teacher-student dual model framework. Our method is featured with
1) the exponential moving averaging strategy to update the teacher from the
student online, 2) using plenty of region proposals and soft pseudo-labels as
the student's training targets, and 3) a light-weighted detection-specific data
ensemble for the teacher to generate more reliable pseudo-labels. Compared to
the recent state-of-the-art -- STAC, which uses hard labels on sparsely
selected hard pseudo samples, the teacher in our model exposes richer
information to the student with soft-labels on many proposals. Our model
achieves COCO-style AP of 53.04% on VOC07 val set, 8.4% better than STAC, when
using VOC12 as unlabeled data. On MS-COCO, it outperforms prior work when only
a small percentage of data is taken as labeled. It also reaches 53.8% AP on
MS-COCO test-dev with 3.1% gain over the fully supervised ResNet-152 Cascaded
R-CNN, by tapping into unlabeled data of a similar size to the labeled data.

我们提出了一种基于师生对抗框架的半监督目标检测方法，使用区域提议和软伪标签作为学生的训练目标，并使用指数移动平均策略更新教师。相比于最近的最先进技术，我们的模型使用许多建议和软标签向学生暴露了更丰富的信息，并通过使用检测特定数据集合，使教师更可靠地生成伪标签。在 VOC07 验证集上达到了 53.04% 的 COCO-style AP，比 STAC 高出 8.4%，在 MS-COCO 上取得了优于以往工作的表现。