  Semi-supervised object detection (SSOD) has made significant progress with
the development of pseudo-label-based end-to-end methods. However, many of
these methods face challenges due to class imbalance, which hinders the
effectiveness of the pseudo-label generator. Furthermore, in the literature, it
has been observed that low-quality pseudo-labels severely limit the performance
of SSOD. In this paper, we examine the root causes of low-quality pseudo-labels
and present novel learning mechanisms to improve the label generation quality.
To cope with high false-negative and low precision rates, we introduce an
adaptive thresholding mechanism that helps the proposed network to filter out
optimal bounding boxes. We further introduce a Jitter-Bagging module to provide
accurate information on localization to help refine the bounding boxes.
Additionally, two new losses are introduced using the background and foreground
scores predicted by the teacher and student networks to improvise the
pseudo-label recall rate. Furthermore, our method applies strict supervision to
the teacher network by feeding strong & weak augmented data to generate robust
pseudo-labels so that it can detect small and complex objects. Finally, the
extensive experiments show that the proposed network outperforms
state-of-the-art methods on MS-COCO and Pascal VOC datasets and allows the
baseline network to achieve 100% supervised performance with much less (i.e.,
20%) labeled data.
