  Convolutional neural networks with many layers have recently been shown to
achieve excellent results on many high-level tasks such as image
classification, object detection and more recently also semantic segmentation.
Particularly for semantic segmentation, a two-stage procedure is often
employed. Hereby, convolutional networks are trained to provide good local
pixel-wise features for the second step being traditionally a more global
graphical model. In this work we unify this two-stage process into a single
joint training algorithm. We demonstrate our method on the semantic image
segmentation task and show encouraging results on the challenging PASCAL VOC
2012 dataset.
