  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.
