Generating semantic segmentation datasets has consistently been laborious and time-consuming, particularly in the context of large models or specialized domains(i.e. Medical Imaging or Remote Sensing). Specifically, large models necessitate a substantial volume of data, while datasets in professional domains frequently require the involvement of domain experts. Both scenarios are susceptible to inaccurate data labeling, which can significantly affect the ultimate performance of the trained model. This paper proposes a simple and effective label pixel-level completion method, \textbf{Label Mask AutoEncoder} (L-MAE), which fully uses the existing information in the label to generate the complete label. The proposed model are the first to apply the Mask Auto-Encoder to downstream tasks. In detail, L-MAE adopts the fusion strategy that stacks the label and the corresponding image, namely fuse map. Moreover, since some of the image information is lost when masking the fuse map, direct reconstruction may lead to poor performance. We proposed Image Patch Supplement algorithm to supplement the missing information during the mask-reconstruct process, and empirically found that an average of 4.1\% mIoU can be improved. We conducted a experiment to evaluate the efficacy of L-MAE to complete the dataset. We employed a degraded Pascal VOC dataset and the degraded dataset enhanced by L-MAE to train an identical conventional semantic segmentation model for the initial set of experiments. The results of these experiments demonstrate a performance enhancement of 13.5\% in the model trained with the L-MAE-enhanced dataset compared to the unenhanced dataset.

翻译：生成语义分割数据集一直是一项耗时费力的工作，尤其在大型模型或专业领域（如医学影像或遥感）中更是如此。具体而言，大型模型需要大量数据，而专业领域的数据集通常需要领域专家的参与。这两种情况都容易受到数据标注不准确的影响，这将显著影响训练模型的最终性能。本文提出一种简单有效的标签像素级补全方法——标签掩码自编码器（L-MAE），该方法充分利用标签中的现有信息来生成完整标签。所提出的模型首次将掩码自编码器应用于下游任务。具体而言，L-MAE采用融合策略，将标签与对应图像堆叠，即融合图。此外，由于在掩码融合图时会丢失部分图像信息，直接重建可能导致性能不佳。我们提出图像块补充算法，以在掩码-重建过程中补充缺失信息，实验发现平均可提升4.1%的mIoU。我们开展实验评估L-MAE补全数据集的效果。使用退化后的Pascal VOC数据集以及经L-MAE增强的退化数据集，训练相同的传统语义分割模型进行初步实验。结果表明，与未增强数据集训练的模型相比，采用L-MAE增强数据集训练的模型性能提升了13.5%。