  We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask
auto-labeling framework for instance segmentation using only box annotations.
MAL takes box-cropped images as inputs and conditionally generates their mask
pseudo-labels.We show that Vision Transformers are good mask auto-labelers. Our
method significantly reduces the gap between auto-labeling and human annotation
regarding mask quality. Instance segmentation models trained using the
MAL-generated masks can nearly match the performance of their fully-supervised
counterparts, retaining up to 97.4\% performance of fully supervised models.
The best model achieves 44.1\% mAP on COCO instance segmentation (test-dev
2017), outperforming state-of-the-art box-supervised methods by significant
margins. Qualitative results indicate that masks produced by MAL are, in some
cases, even better than human annotations.
