In recent years, the need for semantic segmentation has arisen across several
different applications and environments. However, the expense and redundancy of
annotation often limits the quantity of labels available for training in any
domain, while deployment is easier if a single model works well across domains.
In this paper, we pose the novel problem of universal semi-supervised semantic
segmentation and propose a solution framework, to meet the dual needs of lower
annotation and deployment costs. In contrast to counterpoints such as fine
tuning, joint training or unsupervised domain adaptation, universal
semi-supervised segmentation ensures that across all domains: (i) a single
model is deployed, (ii) unlabeled data is used, (iii) performance is improved,
(iv) only a few labels are needed and (v) label spaces may differ. To address
this, we minimize supervised as well as within and cross-domain unsupervised
losses, introducing a novel feature alignment objective based on pixel-aware
entropy regularization for the latter. We demonstrate quantitative advantages
over other approaches on several combinations of segmentation datasets across
different geographies (Germany, England, India) and environments (outdoors,
indoors), as well as qualitative insights on the aligned representations.

本文提出了一种通用的半监督语义分割框架，旨在在各种应用领域实现更低的注释和部署成本，并通过像素感知熵正则化的特征对齐目标来最小化有监督以及无监督损失，以实现在多个领域中都能使用单一模型，利用少量标记实现性能提升，同时可以处理不同领域的标签空间不同的问题。