  The performance of deep networks for semantic image segmentation largely
depends on the availability of large-scale training images which are labelled
at the pixel level. Typically, such pixel-level image labellings are obtained
manually by a labour-intensive process. To alleviate the burden of manual image
labelling, we propose an interesting learning approach to generate pixel-level
image labellings automatically. A Guided Filter Network (GFN) is first
developed to learn the segmentation knowledge from a source domain, and such
GFN then transfers such segmentation knowledge to generate coarse object masks
in the target domain. Such coarse object masks are treated as pseudo labels and
they are further integrated to optimize/refine the GFN iteratively in the
target domain. Our experiments on six image sets have demonstrated that our
proposed approach can generate fine-grained object masks (i.e., pixel-level
object labellings), whose quality is very comparable to the manually-labelled
ones. Our proposed approach can also achieve better performance on semantic
image segmentation than most existing weakly-supervised approaches.
