  Recent advances in semi-supervised object detection (SSOD) are largely driven
by consistency-based pseudo-labeling methods for image classification tasks,
producing pseudo labels as supervisory signals. However, when using pseudo
labels, there is a lack of consideration in localization precision and
amplified class imbalance, both of which are critical for detection tasks. In
this paper, we introduce certainty-aware pseudo labels tailored for object
detection, which can effectively estimate the classification and localization
quality of derived pseudo labels. This is achieved by converting conventional
localization as a classification task followed by refinement. Conditioned on
classification and localization quality scores, we dynamically adjust the
thresholds used to generate pseudo labels and reweight loss functions for each
category to alleviate the class imbalance problem. Extensive experiments
demonstrate that our method improves state-of-the-art SSOD performance by 1-2%
AP on COCO and PASCAL VOC while being orthogonal and complementary to most
existing methods. In the limited-annotation regime, our approach improves
supervised baselines by up to 10% AP using only 1-10% labeled data from COCO.
