  State-of-the-art semantic segmentation approaches increase the receptive
field of their models by using either a downsampling path composed of
poolings/strided convolutions or successive dilated convolutions. However, it
is not clear which operation leads to best results. In this paper, we
systematically study the differences introduced by distinct receptive field
enlargement methods and their impact on the performance of a novel
architecture, called Fully Convolutional DenseResNet (FC-DRN). FC-DRN has a
densely connected backbone composed of residual networks. Following standard
image segmentation architectures, receptive field enlargement operations that
change the representation level are interleaved among residual networks. This
allows the model to exploit the benefits of both residual and dense
connectivity patterns, namely: gradient flow, iterative refinement of
representations, multi-scale feature combination and deep supervision. In order
to highlight the potential of our model, we test it on the challenging CamVid
urban scene understanding benchmark and make the following observations: 1)
downsampling operations outperform dilations when the model is trained from
scratch, 2) dilations are useful during the finetuning step of the model, 3)
coarser representations require less refinement steps, and 4) ResNets (by model
construction) are good regularizers, since they can reduce the model capacity
when needed. Finally, we compare our architecture to alternative methods and
report state-of-the-art result on the Camvid dataset, with at least twice fewer
parameters.
