  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.
