Vision Transformers Are Good Mask Auto-Labelers
Shiyi Lan, Xitong Yang, Zhiding Yu, Zuxuan Wu, Jose M. Alvarez, Anima Anandkumar
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.
BibTeX
@inproceedings{cvpr2023_visiontransforme,
title = {Vision Transformers Are Good Mask Auto-Labelers},
author = {Shiyi Lan and Xitong Yang and Zhiding Yu and Zuxuan Wu and Jose M. Alvarez and Anima Anandkumar},
booktitle = {CVPR 2023},
year = {2023}
}