  In current practice, many image processing tasks are done sequentially (e.g.
denoising, dehazing, followed by semantic segmentation). In this paper, we
propose a novel multi-task neural network architecture designed for combining
sequential image processing tasks. We extend U-Net by additional decoding
pathways for each individual task, and explore deep cascading of outputs and
connectivity from one pathway to another. We demonstrate effectiveness of the
proposed approach on denoising and semantic segmentation, as well as on
progressive coarse-to-fine semantic segmentation, and achieve better
performance than multiple individual or jointly-trained networks, with lower
number of trainable parameters.
