  Single-stage detectors suffer from extreme foreground-background class
imbalance, while two-stage detectors do not. Therefore, in semi-supervised
object detection, two-stage detectors can deliver remarkable performance by
only selecting high-quality pseudo labels based on classification scores.
However, directly applying this strategy to single-stage detectors would
aggravate the class imbalance with fewer positive samples. Thus, single-stage
detectors have to consider both quality and quantity of pseudo labels
simultaneously. In this paper, we design a dynamic self-adaptive threshold
(DSAT) strategy in classification branch, which can automatically select pseudo
labels to achieve an optimal trade-off between quality and quantity. Besides,
to assess the regression quality of pseudo labels in single-stage detectors, we
propose a module to compute the regression uncertainty of boxes based on
Non-Maximum Suppression. By leveraging only 10% labeled data from COCO, our
method achieves 35.0% AP on anchor-free detector (FCOS) and 32.9% on
anchor-based detector (RetinaNet).
