  State-of-the-art systems for semantic image segmentation use feed-forward
pipelines with fixed computational costs. Building an image segmentation system
that works across a range of computational budgets is challenging and
time-intensive as new architectures must be designed and trained for every
computational setting. To address this problem we develop a recurrent neural
network that successively improves prediction quality with each iteration.
Importantly, the RNN may be deployed across a range of computational budgets by
merely running the model for a variable number of iterations. We find that this
architecture is uniquely suited for efficiently segmenting videos. By
exploiting the segmentation of past frames, the RNN can perform video
segmentation at similar quality but reduced computational cost compared to
state-of-the-art image segmentation methods. When applied to static images in
the PASCAL VOC 2012 and Cityscapes segmentation datasets, the RNN traces out a
speed-accuracy curve that saturates near the performance of state-of-the-art
segmentation methods.
