  Recently, very deep convolutional neural networks (CNNs) have shown
outstanding performance in object recognition and have also been the first
choice for dense classification problems such as semantic segmentation.
However, repeated subsampling operations like pooling or convolution striding
in deep CNNs lead to a significant decrease in the initial image resolution.
Here, we present RefineNet, a generic multi-path refinement network that
explicitly exploits all the information available along the down-sampling
process to enable high-resolution prediction using long-range residual
connections. In this way, the deeper layers that capture high-level semantic
features can be directly refined using fine-grained features from earlier
convolutions. The individual components of RefineNet employ residual
connections following the identity mapping mindset, which allows for effective
end-to-end training. Further, we introduce chained residual pooling, which
captures rich background context in an efficient manner. We carry out
comprehensive experiments and set new state-of-the-art results on seven public
datasets. In particular, we achieve an intersection-over-union score of 83.4 on
the challenging PASCAL VOC 2012 dataset, which is the best reported result to
date.
