Referring Expression Segmentation (RES) is an emerging task in computer
vision, which segments the target instances in images based on text
descriptions. However, its development is plagued by the expensive segmentation
labels. To address this issue, we propose a new learning task for RES called
Omni-supervised Referring Expression Segmentation (Omni-RES), which aims to
make full use of unlabeled, fully labeled and weakly labeled data, e.g.,
referring points or grounding boxes, for efficient RES training. To accomplish
this task, we also propose a novel yet strong baseline method for Omni-RES
based on the recently popular teacher-student learning, where where the weak
labels are not directly transformed into supervision signals but used as a
yardstick to select and refine high-quality pseudo-masks for teacher-student
learning. To validate the proposed Omni-RES method, we apply it to a set of
state-of-the-art RES models and conduct extensive experiments on a bunch of RES
datasets. The experimental results yield the obvious merits of Omni-RES than
the fully-supervised and semi-supervised training schemes. For instance, with
only 10% fully labeled data, Omni-RES can help the base model achieve 100%
fully supervised performance, and it also outperform the semi-supervised
alternative by a large margin, e.g., +14.93% on RefCOCO and +14.95% on
RefCOCO+, respectively. More importantly, Omni-RES also enable the use of
large-scale vision-langauges like Visual Genome to facilitate low-cost RES
training, and achieve new SOTA performance of RES, e.g., 80.66 on RefCOCO.

基于语义表达分割（RES）的任务，提出一种新的学习任务，称为全监督引用表达分割（Omni-RES），旨在充分利用未标记、完全标记和弱标记数据进行高效的 RES 训练，并基于最近流行的师生学习方法提出一种有效的基准方法来解决监督信号不直接依赖于弱标签的问题。实验证明，Omni-RES 方法明显优于全监督和半监督训练方案，并使得大规模视觉语言如 Visual Genome 能够在低成本下进行 RES 训练，实现了新的最佳效果。