  Semantic image segmentation is a principal problem in computer vision, where
the aim is to correctly classify each individual pixel of an image into a
semantic label. Its widespread use in many areas, including medical imaging and
autonomous driving, has fostered extensive research in recent years. Empirical
improvements in tackling this task have primarily been motivated by successful
exploitation of Convolutional Neural Networks (CNNs) pre-trained for image
classification and object recognition. However, the pixel-wise labelling with
CNNs has its own unique challenges: (1) an accurate deconvolution, or
upsampling, of low-resolution output into a higher-resolution segmentation mask
and (2) an inclusion of global information, or context, within locally
extracted features. To address these issues, we propose a novel architecture to
conduct the equivalent of the deconvolution operation globally and acquire
dense predictions. We demonstrate that it leads to improved performance of
state-of-the-art semantic segmentation models on the PASCAL VOC 2012 benchmark,
reaching 74.0% mean IU accuracy on the test set.
