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
arXiv PDF paper https://arxiv.org/pdf/2301.03992.pdf
... 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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