  In contrast to fully supervised methods using pixel-wise mask labels,
box-supervised instance segmentation takes advantage of simple box annotations,
which has recently attracted increasing research attention. This paper presents
a novel single-shot instance segmentation approach, namely Box2Mask, which
integrates the classical level-set evolution model into deep neural network
learning to achieve accurate mask prediction with only bounding box
supervision. Specifically, both the input image and its deep features are
employed to evolve the level-set curves implicitly, and a local consistency
module based on a pixel affinity kernel is used to mine the local context and
spatial relations. Two types of single-stage frameworks, i.e., CNN-based and
transformer-based frameworks, are developed to empower the level-set evolution
for box-supervised instance segmentation, and each framework consists of three
essential components: instance-aware decoder, box-level matching assignment and
level-set evolution. By minimizing the level-set energy function, the mask map
of each instance can be iteratively optimized within its bounding box
annotation. The experimental results on five challenging testbeds, covering
general scenes, remote sensing, medical and scene text images, demonstrate the
outstanding performance of our proposed Box2Mask approach for box-supervised
instance segmentation. In particular, with the Swin-Transformer large backbone,
our Box2Mask obtains 42.4% mask AP on COCO, which is on par with the recently
developed fully mask-supervised methods. The code is available at:
https://github.com/LiWentomng/boxlevelset.
