  We propose a method for high-performance semantic image segmentation (or
semantic pixel labelling) based on very deep residual networks, which achieves
the state-of-the-art performance. A few design factors are carefully considered
to this end.
  We make the following contributions. (i) First, we evaluate different
variations of a fully convolutional residual network so as to find the best
configuration, including the number of layers, the resolution of feature maps,
and the size of field-of-view. Our experiments show that further enlarging the
field-of-view and increasing the resolution of feature maps are typically
beneficial, which however inevitably leads to a higher demand for GPU memories.
To walk around the limitation, we propose a new method to simulate a high
resolution network with a low resolution network, which can be applied during
training and/or testing. (ii) Second, we propose an online bootstrapping method
for training. We demonstrate that online bootstrapping is critically important
for achieving good accuracy. (iii) Third we apply the traditional dropout to
some of the residual blocks, which further improves the performance. (iv)
Finally, our method achieves the currently best mean intersection-over-union
78.3\% on the PASCAL VOC 2012 dataset, as well as on the recent dataset
Cityscapes.
