Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images

• URL: http://arxiv.org/abs/2104.01526v1
• Date: Sun, 4 Apr 2021 03:01:52 GMT
• Title: Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images
• Authors: Xinggang Wang and Jiapei Feng and Bin Hu and Qi Ding and Longjin Ran and Xiaoxin Chen and Wenyu Liu
• Abstract summary: We propose a box-supervised class-agnostic object segmentation (BoxCaseg) based solution for weakly-supervised instance segmentation. The BoxCaseg model is jointly trained using box-supervised images and salient images in a multi-task learning manner. The weakly-supervised Mask R-CNN is on par with fully-supervised Mask R-CNN on PASCAL VOC and significantly outperforms previous state-of-the-art box-supervised instance segmentation methods on COCO.
• Score: 34.498542318157284
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans have a strong class-agnostic object segmentation ability and can outline boundaries of unknown objects precisely, which motivates us to propose a box-supervised class-agnostic object segmentation (BoxCaseg) based solution for weakly-supervised instance segmentation. The BoxCaseg model is jointly trained using box-supervised images and salient images in a multi-task learning manner. The fine-annotated salient images provide class-agnostic and precise object localization guidance for box-supervised images. The object masks predicted by a pretrained BoxCaseg model are refined via a novel merged and dropped strategy as proxy ground truth to train a Mask R-CNN for weakly-supervised instance segmentation. Only using $7991$ salient images, the weakly-supervised Mask R-CNN is on par with fully-supervised Mask R-CNN on PASCAL VOC and significantly outperforms previous state-of-the-art box-supervised instance segmentation methods on COCO. The source code, pretrained models and datasets are available at \url{https://github.com/hustvl/BoxCaseg}.

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