  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.
