  Deep learning for unsupervised image segmentation remains challenging due to
the absence of human labels. The common idea is to train a segmentation head,
with the supervision of pixel-wise pseudo-labels generated based on the
representation of self-supervised backbones. By doing so, the model performance
depends much on the distance between the distributions of target datasets and
the pre-training dataset (e.g., ImageNet). In this work, we investigate a new
task, namely unsupervised camouflaged object segmentation (UCOS), where the
target objects own a common rarely-seen attribute, i.e., camouflage.
Unsurprisingly, we find that the state-of-the-art unsupervised models struggle
in adapting UCOS, due to the domain gap between the properties of generic and
camouflaged objects. To this end, we formulate the UCOS as a source-free
unsupervised domain adaptation task (UCOS-DA), where both source labels and
target labels are absent during the whole model training process. Specifically,
we define a source model consisting of self-supervised vision transformers
pre-trained on ImageNet. On the other hand, the target domain includes a simple
linear layer (i.e., our target model) and unlabeled camouflaged objects. We
then design a pipeline for foreground-background-contrastive self-adversarial
domain adaptation, to achieve robust UCOS. As a result, our baseline model
achieves superior segmentation performance when compared with competing
unsupervised models on the UCOS benchmark, with the training set which's scale
is only one tenth of the supervised COS counterpart.
