  The rapid development of deep learning has made a great progress in image
segmentation, one of the fundamental tasks of computer vision. However, the
current segmentation algorithms mostly rely on the availability of pixel-level
annotations, which are often expensive, tedious, and laborious. To alleviate
this burden, the past years have witnessed an increasing attention in building
label-efficient, deep-learning-based image segmentation algorithms. This paper
offers a comprehensive review on label-efficient image segmentation methods. To
this end, we first develop a taxonomy to organize these methods according to
the supervision provided by different types of weak labels (including no
supervision, inexact supervision, incomplete supervision and inaccurate
supervision) and supplemented by the types of segmentation problems (including
semantic segmentation, instance segmentation and panoptic segmentation). Next,
we summarize the existing label-efficient image segmentation methods from a
unified perspective that discusses an important question: how to bridge the gap
between weak supervision and dense prediction -- the current methods are mostly
based on heuristic priors, such as cross-pixel similarity, cross-label
constraint, cross-view consistency, and cross-image relation. Finally, we share
our opinions about the future research directions for label-efficient deep
image segmentation.
