  This paper presents a "learning to learn" approach to figure-ground image
segmentation. By exploring webly-abundant images of specific visual effects,
our method can effectively learn the visual-effect internal representations in
an unsupervised manner and uses this knowledge to differentiate the figure from
the ground in an image. Specifically, we formulate the meta-learning process as
a compositional image editing task that learns to imitate a certain visual
effect and derive the corresponding internal representation. Such a generative
process can help instantiate the underlying figure-ground notion and enables
the system to accomplish the intended image segmentation. Whereas existing
generative methods are mostly tailored to image synthesis or style transfer,
our approach offers a flexible learning mechanism to model a general concept of
figure-ground segmentation from unorganized images that have no explicit
pixel-level annotations. We validate our approach via extensive experiments on
six datasets to demonstrate that the proposed model can be end-to-end trained
without ground-truth pixel labeling yet outperforms the existing methods of
unsupervised segmentation tasks.
