Progress in self-supervised learning has brought strong general image
representation learning methods. Yet so far, it has mostly focused on
image-level learning. In turn, tasks such as unsupervised image segmentation
have not benefited from this trend as they require spatially-diverse
representations. However, learning dense representations is challenging, as in
the unsupervised context it is not clear how to guide the model to learn
representations that correspond to various potential object categories. In this
paper, we argue that self-supervised learning of object parts is a solution to
this issue. Object parts are generalizable: they are a priori independent of an
object definition, but can be grouped to form objects a posteriori. To this
end, we leverage the recently proposed Vision Transformer's capability of
attending to objects and combine it with a spatially dense clustering task for
fine-tuning the spatial tokens. Our method surpasses the state-of-the-art on
three semantic segmentation benchmarks by 17%-3%, showing that our
representations are versatile under various object definitions. Finally, we
extend this to fully unsupervised segmentation - which refrains completely from
using label information even at test-time - and demonstrate that a simple
method for automatically merging discovered object parts based on community
detection yields substantial gains.

本文介绍了以往自我监督的学习方式大多聚焦于图像级的表示学习，不能为无监督图像分割等需要空间多样的表示的任务带来改进，通过自我监督学习物体部件提出了新的解决途径，并结合目前趋势的 Vision Transformer，利用稠密聚类任务进行空间标记的微调，取得了在语义分割基准测试上超过 17%-3% 的最先进结果，且在完全无监督分割方面也具有重大潜力。