  We introduce a purely feed-forward architecture for semantic segmentation. We
map small image elements (superpixels) to rich feature representations
extracted from a sequence of nested regions of increasing extent. These regions
are obtained by "zooming out" from the superpixel all the way to scene-level
resolution. This approach exploits statistical structure in the image and in
the label space without setting up explicit structured prediction mechanisms,
and thus avoids complex and expensive inference. Instead superpixels are
classified by a feedforward multilayer network. Our architecture achieves new
state of the art performance in semantic segmentation, obtaining 64.4% average
accuracy on the PASCAL VOC 2012 test set.
