  Designed as extremely deep architectures, deep residual networks which
provide a rich visual representation and offer robust convergence behaviors
have recently achieved exceptional performance in numerous computer vision
problems. Being directly applied to a scene labeling problem, however, they
were limited to capture long-range contextual dependence, which is a critical
aspect. To address this issue, we propose a novel approach, Contextual
Recurrent Residual Networks (CRRN) which is able to simultaneously handle rich
visual representation learning and long-range context modeling within a fully
end-to-end deep network. Furthermore, our proposed end-to-end CRRN is
completely trained from scratch, without using any pre-trained models in
contrast to most existing methods usually fine-tuned from the state-of-the-art
pre-trained models, e.g. VGG-16, ResNet, etc. The experiments are conducted on
four challenging scene labeling datasets, i.e. SiftFlow, CamVid, Stanford
background and SUN datasets, and compared against various state-of-the-art
scene labeling methods.
