  In this paper, we delve into semi-supervised object detection where unlabeled
images are leveraged to break through the upper bound of fully-supervised
object detection models. Previous semi-supervised methods based on pseudo
labels are severely degenerated by noise and prone to overfit to noisy labels,
thus are deficient in learning different unlabeled knowledge well. To address
this issue, we propose a data-uncertainty guided multi-phase learning method
for semi-supervised object detection. We comprehensively consider divergent
types of unlabeled images according to their difficulty levels, utilize them in
different phases and ensemble models from different phases together to generate
ultimate results. Image uncertainty guided easy data selection and region
uncertainty guided RoI Re-weighting are involved in multi-phase learning and
enable the detector to concentrate on more certain knowledge. Through extensive
experiments on PASCAL VOC and MS COCO, we demonstrate that our method behaves
extraordinarily compared to baseline approaches and outperforms them by a large
margin, more than 3% on VOC and 2% on COCO.
