  Despite the availability of large datasets for tasks like image
classification and image-text alignment, labeled data for more complex
recognition tasks, such as detection and segmentation, is less abundant. In
particular, for instance segmentation annotations are time-consuming to
produce, and the distribution of instances is often highly skewed across
classes. While semi-supervised teacher-student distillation methods show
promise in leveraging vast amounts of unlabeled data, they suffer from
miscalibration, resulting in overconfidence in frequently represented classes
and underconfidence in rarer ones. Additionally, these methods encounter
difficulties in efficiently learning from a limited set of examples. We
introduce a dual-strategy to enhance the teacher model's training process,
substantially improving the performance on few-shot learning. Secondly, we
propose a calibration correction mechanism that that enables the student model
to correct the teacher's calibration errors. Using our approach, we observed
marked improvements over a state-of-the-art supervised baseline performance on
the LVIS dataset, with an increase of 2.8% in average precision (AP) and 10.3%
gain in AP for rare classes.
