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.

通过引入一种双重策略来增强教师模型的训练过程，从而显著改善少样本学习，同时提出一种校准校正机制，使学生模型能够纠正教师的校准错误。实验结果在 LVIS 数据集上表现出显著的提升，平均精度（AP）提高了 2.8％，稀有类别的 AP 提高了 10.3％。