  In this paper, we study the problem of unsupervised object segmentation from
single images. We do not introduce a new algorithm, but systematically
investigate the effectiveness of existing unsupervised models on challenging
real-world images. We first introduce seven complexity factors to
quantitatively measure the distributions of background and foreground object
biases in appearance and geometry for datasets with human annotations. With the
aid of these factors, we empirically find that, not surprisingly, existing
unsupervised models fail to segment generic objects in real-world images,
although they can easily achieve excellent performance on numerous simple
synthetic datasets, due to the vast gap in objectness biases between synthetic
and real images. By conducting extensive experiments on multiple groups of
ablated real-world datasets, we ultimately find that the key factors underlying
the failure of existing unsupervised models on real-world images are the
challenging distributions of background and foreground object biases in
appearance and geometry. Because of this, the inductive biases introduced in
existing unsupervised models can hardly capture the diverse object
distributions. Our research results suggest that future work should exploit
more explicit objectness biases in the network design.
