In this work, we present a fully self-supervised framework for semantic
segmentation(FS^4). A fully bootstrapped strategy for semantic segmentation,
which saves efforts for the huge amount of annotation, is crucial for building
customized models from end-to-end for open-world domains. This application is
eagerly needed in realistic scenarios. Even though recent self-supervised
semantic segmentation methods have gained great progress, these works however
heavily depend on the fully-supervised pretrained model and make it impossible
a fully self-supervised pipeline. To solve this problem, we proposed a
bootstrapped training scheme for semantic segmentation, which fully leveraged
the global semantic knowledge for self-supervision with our proposed PGG
strategy and CAE module. In particular, we perform pixel clustering and
assignments for segmentation supervision. Preventing it from clustering a mess,
we proposed 1) a pyramid-global-guided (PGG) training strategy to supervise the
learning with pyramid image/patch-level pseudo labels, which are generated by
grouping the unsupervised features. The stable global and pyramid semantic
pseudo labels can prevent the segmentation from learning too many clutter
regions or degrading to one background region; 2) in addition, we proposed
context-aware embedding (CAE) module to generate global feature embedding in
view of its neighbors close both in space and appearance in a non-trivial way.
We evaluate our method on the large-scale COCO-Stuff dataset and achieved 7.19
mIoU improvements on both things and stuff objects

我们提出了一个基于自我监督的完全自我监督语义分割框架，其中包括金字塔全局引导培训策略和上下文感知嵌入模块等组成部分，显著改善了 COCO-Stuff 数据集上物品和物品分类的性能。