  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}.
