  We present Deep Region Competition (DRC), an algorithm designed to extract
foreground objects from images in a fully unsupervised manner. Foreground
extraction can be viewed as a special case of generic image segmentation that
focuses on identifying and disentangling objects from the background. In this
work, we rethink the foreground extraction by reconciling energy-based prior
with generative image modeling in the form of Mixture of Experts (MoE), where
we further introduce the learned pixel re-assignment as the essential inductive
bias to capture the regularities of background regions. With this modeling, the
foreground-background partition can be naturally found through
Expectation-Maximization (EM). We show that the proposed method effectively
exploits the interaction between the mixture components during the partitioning
process, which closely connects to region competition, a seminal approach for
generic image segmentation. Experiments demonstrate that DRC exhibits more
competitive performances on complex real-world data and challenging
multi-object scenes compared with prior methods. Moreover, we show empirically
that DRC can potentially generalize to novel foreground objects even from
categories unseen during training.
