  This paper presents a semi-supervised learning framework for a customized
semantic segmentation task using multiview image streams. A key challenge of
the customized task lies in the limited accessibility of the labeled data due
to the requirement of prohibitive manual annotation effort. We hypothesize that
it is possible to leverage multiview image streams that are linked through the
underlying 3D geometry, which can provide an additional supervisionary signal
to train a segmentation model. We formulate a new cross-supervision method
using a shape belief transfer---the segmentation belief in one image is used to
predict that of the other image through epipolar geometry analogous to
shape-from-silhouette. The shape belief transfer provides the upper and lower
bounds of the segmentation for the unlabeled data where its gap approaches
asymptotically to zero as the number of the labeled views increases. We
integrate this theory to design a novel network that is agnostic to camera
calibration, network model, and semantic category and bypasses the intermediate
process of suboptimal 3D reconstruction. We validate this network by
recognizing a customized semantic category per pixel from realworld visual data
including non-human species and a subject of interest in social videos where
attaining large-scale annotation data is infeasible.
