  Supervised learning based object detection frameworks demand plenty of
laborious manual annotations, which may not be practical in real applications.
Semi-supervised object detection (SSOD) can effectively leverage unlabeled data
to improve the model performance, which is of great significance for the
application of object detection models. In this paper, we revisit SSOD and
propose Instant-Teaching, a completely end-to-end and effective SSOD framework,
which uses instant pseudo labeling with extended weak-strong data augmentations
for teaching during each training iteration. To alleviate the confirmation bias
problem and improve the quality of pseudo annotations, we further propose a
co-rectify scheme based on Instant-Teaching, denoted as Instant-Teaching$^*$.
Extensive experiments on both MS-COCO and PASCAL VOC datasets substantiate the
superiority of our framework. Specifically, our method surpasses
state-of-the-art methods by 4.2 mAP on MS-COCO when using $2\%$ labeled data.
Even with full supervised information of MS-COCO, the proposed method still
outperforms state-of-the-art methods by about 1.0 mAP. On PASCAL VOC, we can
achieve more than 5 mAP improvement by applying VOC07 as labeled data and VOC12
as unlabeled data.
