Pseudo-labeling is significant for semi-supervised instance segmentation, which generates instance masks and classes from unannotated images for subsequent training. However, in existing pipelines, pseudo-labels that contain valuable information may be directly filtered out due to mismatches in class and mask quality. To address this issue, we propose a novel framework, called pseudo-label aligning instance segmentation (PAIS), in this paper. In PAIS, we devise a dynamic aligning loss (DALoss) that adjusts the weights of semi-supervised loss terms with varying class and mask score pairs. Through extensive experiments conducted on the COCO and Cityscapes datasets, we demonstrate that PAIS is a promising framework for semi-supervised instance segmentation, particularly in cases where labeled data is severely limited. Notably, with just 1\% labeled data, PAIS achieves 21.2 mAP (based on Mask-RCNN) and 19.9 mAP (based on K-Net) on the COCO dataset, outperforming the current state-of-the-art model, \ie, NoisyBoundary with 7.7 mAP, by a margin of over 12 points. Code is available at: \url{https://github.com/hujiecpp/PAIS}.

翻译：伪标签法在半监督实例分割中具有重要意义，它从未标注图像中生成实例掩码和类别以用于后续训练。然而，在现有流程中，包含有价值信息的伪标签可能因类别与掩码质量不匹配而被直接过滤。为解决该问题，本文提出了一种名为伪标签对齐实例分割（PAIS）的新框架。在PAIS中，我们设计了动态对齐损失（DALoss），该损失根据不同的类别与掩码得分对调整半监督损失项的权重。通过在COCO和Cityscapes数据集上的大量实验，我们证明PAIS是一种有前景的半监督实例分割框架，尤其在标注数据极度稀缺的情况下。值得注意的是，仅使用1%的标注数据，PAIS在COCO数据集上基于Mask-RCNN实现了21.2 mAP，基于K-Net实现了19.9 mAP，大幅领先当前最先进模型NoisyBoundary（7.7 mAP）超过12个百分点。代码开源地址：\url{https://github.com/hujiecpp/PAIS}。