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arXiv · 2301.03992

Vision Transformers Are Good Mask Auto-Labelers

Shiyi Lan·Xitong Yang·Zhiding Yu·Zuxuan Wu·Jose M. Alvarez·Anima Anandkumar
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Abstract

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.

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Shiyi Lan, Xitong Yang, Zhiding Yu, Zuxuan Wu, Jose M. Alvarez, Anima Anandkumar. 2023-01-10. Vision Transformers Are Good Mask Auto-Labelers. https://arxiv.org/abs/2301.03992

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