  We present a recurrent model for semantic instance segmentation that
sequentially generates binary masks and their associated class probabilities
for every object in an image. Our proposed system is trainable end-to-end from
an input image to a sequence of labeled masks and, compared to methods relying
on object proposals, does not require post-processing steps on its output. We
study the suitability of our recurrent model on three different instance
segmentation benchmarks, namely Pascal VOC 2012, CVPPP Plant Leaf Segmentation
and Cityscapes. Further, we analyze the object sorting patterns generated by
our model and observe that it learns to follow a consistent pattern, which
correlates with the activations learned in the encoder part of our network.
Source code and models are available at https://imatge-upc.github.io/rsis/
