  We propose a novel semantic segmentation algorithm by learning a
deconvolution network. We learn the network on top of the convolutional layers
adopted from VGG 16-layer net. The deconvolution network is composed of
deconvolution and unpooling layers, which identify pixel-wise class labels and
predict segmentation masks. We apply the trained network to each proposal in an
input image, and construct the final semantic segmentation map by combining the
results from all proposals in a simple manner. The proposed algorithm mitigates
the limitations of the existing methods based on fully convolutional networks
by integrating deep deconvolution network and proposal-wise prediction; our
segmentation method typically identifies detailed structures and handles
objects in multiple scales naturally. Our network demonstrates outstanding
performance in PASCAL VOC 2012 dataset, and we achieve the best accuracy
(72.5%) among the methods trained with no external data through ensemble with
the fully convolutional network.
