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Train object segmentation with only boxes by using transformers as mask auto-labeler with MAL

Train object segmentation with only boxes by using transformers as mask auto-labeler with MAL


Vision Transformers Are Good Mask Auto-Labelers

arXiv paper abstract https://arxiv.org/abs/2301.03992



... 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 ... show that Vision Transformers are good mask auto-labelers.


... 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.



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