  This study delves into semi-supervised object detection (SSOD) to improve
detector performance with additional unlabeled data. State-of-the-art SSOD
performance has been achieved recently by self-training, in which training
supervision consists of ground truths and pseudo-labels. In current studies, we
observe that class imbalance in SSOD severely impedes the effectiveness of
self-training. To address the class imbalance, we propose adaptive
class-rebalancing self-training (ACRST) with a novel memory module called
CropBank. ACRST adaptively rebalances the training data with foreground
instances extracted from the CropBank, thereby alleviating the class imbalance.
Owing to the high complexity of detection tasks, we observe that both
self-training and data-rebalancing suffer from noisy pseudo-labels in SSOD.
Therefore, we propose a novel two-stage filtering algorithm to generate
accurate pseudo-labels. Our method achieves satisfactory improvements on
MS-COCO and VOC benchmarks. When using only 1\% labeled data in MS-COCO, our
method achieves 17.02 mAP improvement over supervised baselines, and 5.32 mAP
improvement compared with state-of-the-art methods.
