  Semantic segmentation is critical to image content understanding and object
localization. Recent development in fully-convolutional neural network (FCN)
has enabled accurate pixel-level labeling. One issue in previous works is that
the FCN based method does not exploit the object boundary information to
delineate segmentation details since the object boundary label is ignored in
the network training. To tackle this problem, we introduce a double branch
fully convolutional neural network, which separates the learning of the
desirable semantic class labeling with mask-level object proposals guided by
relabeled boundaries. This network, called object boundary guided FCN
(OBG-FCN), is able to integrate the distinct properties of object shape and
class features elegantly in a fully convolutional way with a designed masking
architecture. We conduct experiments on the PASCAL VOC segmentation benchmark,
and show that the end-to-end trainable OBG-FCN system offers great improvement
in optimizing the target semantic segmentation quality.
